�ݺ�ߣshows by User: xllora

�ݺ�ߣshows by User: xllora / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: xllora / Thu, 15 Jul 2010 14:01:26 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: xllora Meandre 2.0 Alpha Preview /slideshow/meandre-20-alpha-preview/4765637 meandre2-0alpha-preview-100715140140-phpapp01
A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB]]>
A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB]]> Thu, 15 Jul 2010 14:01:26 GMT /slideshow/meandre-20-alpha-preview/4765637 xllora@slideshare.net(xllora) Meandre 2.0 Alpha Preview xllora A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/meandre2-0alpha-preview-100715140140-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB

Meandre 2.0 Alpha Preview from Xavier Llorà

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0 Soaring the Clouds with Meandre /slideshow/soaring-the-clouds-with-meandre/3440242 dir-workshop-20100315-100315174055-phpapp01
Description of NCSA's cloud effort and how to orchestrate clouds using meandre]]>
Description of NCSA's cloud effort and how to orchestrate clouds using meandre]]> Mon, 15 Mar 2010 17:40:50 GMT /slideshow/soaring-the-clouds-with-meandre/3440242 xllora@slideshare.net(xllora) Soaring the Clouds with Meandre xllora Description of NCSA's cloud effort and how to orchestrate clouds using meandre <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dir-workshop-20100315-100315174055-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Description of NCSA's cloud effort and how to orchestrate clouds using meandre

Soaring the Clouds with Meandre from Xavier Llorà

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0 From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0 /slideshow/from-galapagos-to-twitter-darwin-natural-selection-and-web-20/2019013 monmouth-talk-slides-090918130141-phpapp01
One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems.]]>
One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems.]]> Fri, 18 Sep 2009 13:01:30 GMT /slideshow/from-galapagos-to-twitter-darwin-natural-selection-and-web-20/2019013 xllora@slideshare.net(xllora) From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0 xllora One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/monmouth-talk-slides-090918130141-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems.

From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0 from Xavier Llorà

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0 Large Scale Data Mining using Genetics-Based Machine Learning /slideshow/large-scale-data-mining-using-geneticsbased-machine-learning/1727172 gecco2009largegbmltutorial-090715163244-phpapp01
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.]]>
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.]]> Wed, 15 Jul 2009 16:32:36 GMT /slideshow/large-scale-data-mining-using-geneticsbased-machine-learning/1727172 xllora@slideshare.net(xllora) Large Scale Data Mining using Genetics-Based Machine Learning xllora We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gecco2009largegbmltutorial-090715163244-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.

Large Scale Data Mining using Genetics-Based Machine Learning from Xavier Llorà

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0 Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre /slideshow/dataintensive-computing-for-competent-genetic-algorithms-a-pilot-study-using-meandre/1717843 gecco2009-meandre-090713230644-phpapp02
Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.]]>
Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.]]> Mon, 13 Jul 2009 23:06:40 GMT /slideshow/dataintensive-computing-for-competent-genetic-algorithms-a-pilot-study-using-meandre/1717843 xllora@slideshare.net(xllora) Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre xllora Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gecco2009-meandre-090713230644-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.

Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre from Xavier Llorà

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0 Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends /xllora/scalabiltity-in-gbml-accuracybased-michigan-fuzzy-lcs-and-new-trends nigel-2006-casillas-090608160722-phpapp02
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends]]>
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends]]> Mon, 08 Jun 2009 16:07:19 GMT /xllora/scalabiltity-in-gbml-accuracybased-michigan-fuzzy-lcs-and-new-trends xllora@slideshare.net(xllora) Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends xllora Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-casillas-090608160722-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends

Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new Trends from Xavier Llorà

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0 Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Scalability and Explanatory Power /slideshow/pittsburgh-learning-classifier-systems-for-protein-structure-prediction-scalability-and-explanatory-power/1384657 nigel-2006-bacardit-090504154202-phpapp02
Jaume Bacardit explores the usage of GBML for protein structure prediction]]>
Jaume Bacardit explores the usage of GBML for protein structure prediction]]> Mon, 04 May 2009 15:41:55 GMT /slideshow/pittsburgh-learning-classifier-systems-for-protein-structure-prediction-scalability-and-explanatory-power/1384657 xllora@slideshare.net(xllora) Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Scalability and Explanatory Power xllora Jaume Bacardit explores the usage of GBML for protein structure prediction <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-bacardit-090504154202-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Jaume Bacardit explores the usage of GBML for protein structure prediction

Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Scalability and Explanatory Power from Xavier Llorà

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0 Towards a Theoretical Towards a Theoretical Framework for LCS Framework for LCS /slideshow/towards-a-theoretical-towards-a-theoretical-framework-for-lcs-framework-for-lcs/1384652 nigel-2006-barry-090504154054-phpapp01
Alwyn Barry introduces the theoretical framework for LCS that Jan Drugowitsch is currently working on.]]>
Alwyn Barry introduces the theoretical framework for LCS that Jan Drugowitsch is currently working on.]]> Mon, 04 May 2009 15:40:45 GMT /slideshow/towards-a-theoretical-towards-a-theoretical-framework-for-lcs-framework-for-lcs/1384652 xllora@slideshare.net(xllora) Towards a Theoretical Towards a Theoretical Framework for LCS Framework for LCS xllora Alwyn Barry introduces the theoretical framework for LCS that Jan Drugowitsch is currently working on. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-barry-090504154054-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Alwyn Barry introduces the theoretical framework for LCS that Jan Drugowitsch is currently working on.

Towards a Theoretical Towards a Theoretical Framework for LCS Framework for LCS from Xavier Llorà

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0 Learning Classifier Systems for Class Imbalance Problems /slideshow/learning-classifier-systems-for-class-imbalance-problems/1384643 nigel-2006-bernado-090504153926-phpapp02
Ester Bernadó-Mansilla analyzes the behavior of LCS on extreme class imbalance problems]]>
Ester Bernadó-Mansilla analyzes the behavior of LCS on extreme class imbalance problems]]> Mon, 04 May 2009 15:39:20 GMT /slideshow/learning-classifier-systems-for-class-imbalance-problems/1384643 xllora@slideshare.net(xllora) Learning Classifier Systems for Class Imbalance Problems xllora Ester Bernadó-Mansilla analyzes the behavior of LCS on extreme class imbalance problems <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-bernado-090504153926-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Ester Bernadó-Mansilla analyzes the behavior of LCS on extreme class imbalance problems

Learning Classifier Systems for Class Imbalance Problems from Xavier Llorà

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0 A Retrospective Look at A Retrospective Look at Classiﬁer System ResearchClassiﬁer System Research /slideshow/a-retrospective-look-at-a-retrospective-look-at-classier-system-researchclassier-system-research/1384637 nigel-2006-booker-090504153739-phpapp02
Lashon Booker presents the glance to the past of LCS and how that connects to the current and future efforts.]]>
Lashon Booker presents the glance to the past of LCS and how that connects to the current and future efforts.]]> Mon, 04 May 2009 15:37:30 GMT /slideshow/a-retrospective-look-at-a-retrospective-look-at-classier-system-researchclassier-system-research/1384637 xllora@slideshare.net(xllora) A Retrospective Look at A Retrospective Look at Classiﬁer System ResearchClassiﬁer System Research xllora Lashon Booker presents the glance to the past of LCS and how that connects to the current and future efforts. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-booker-090504153739-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Lashon Booker presents the glance to the past of LCS and how that connects to the current and future efforts.

A Retrospective Look at A Retrospective Look at Classiﬁer System ResearchClassiﬁer System Research from Xavier Llorà

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0 XCS: Current capabilities and future challenges /slideshow/xcs-current-capabilities-and-future-challenges/1384628 nigel-2006-butz-090504153553-phpapp02
Martin Butz presents the current state-of-the-union of XCS]]>
Martin Butz presents the current state-of-the-union of XCS]]> Mon, 04 May 2009 15:35:49 GMT /slideshow/xcs-current-capabilities-and-future-challenges/1384628 xllora@slideshare.net(xllora) XCS: Current capabilities and future challenges xllora Martin Butz presents the current state-of-the-union of XCS <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-butz-090504153553-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Martin Butz presents the current state-of-the-union of XCS

XCS: Current capabilities and future challenges from Xavier Llorà

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0 Negative Selection for Algorithm for Anomaly Detection /slideshow/negative-selection-for-algorithm-for-anomaly-detection/1384601 nigel-2006-dasgupta-090504153353-phpapp01
Dipankar Dasgupta reviews the negative selection algorithm and its connections to learning classifier systems]]>
Dipankar Dasgupta reviews the negative selection algorithm and its connections to learning classifier systems]]> Mon, 04 May 2009 15:32:20 GMT /slideshow/negative-selection-for-algorithm-for-anomaly-detection/1384601 xllora@slideshare.net(xllora) Negative Selection for Algorithm for Anomaly Detection xllora Dipankar Dasgupta reviews the negative selection algorithm and its connections to learning classifier systems <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-dasgupta-090504153353-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Dipankar Dasgupta reviews the negative selection algorithm and its connections to learning classifier systems

Negative Selection for Algorithm for Anomaly Detection from Xavier Llorà

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0 Searle, Intentionality, and the Future of Classifier Systems /slideshow/searle-intentionality-and-the-future-of-classifier-systems/1384594 nigel-2006-goldberg-090504153108-phpapp02
David E. Goldberg reflects about the reality of social constructs and the future of learning classifier systems]]>
David E. Goldberg reflects about the reality of social constructs and the future of learning classifier systems]]> Mon, 04 May 2009 15:30:47 GMT /slideshow/searle-intentionality-and-the-future-of-classifier-systems/1384594 xllora@slideshare.net(xllora) Searle, Intentionality, and the Future of Classifier Systems xllora David E. Goldberg reflects about the reality of social constructs and the future of learning classifier systems <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-goldberg-090504153108-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> David E. Goldberg reflects about the reality of social constructs and the future of learning classifier systems

Searle, Intentionality, and the Future of Classifier Systems from Xavier Llorà

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0 Computed Prediction: So far, so good. What now? /slideshow/computed-prediction-so-far-so-good-what-now/1384584 nigel-2006-lanzi-090504152951-phpapp02
Pier Luca Lanzi talks at NIGEL 2006 about computed predictions]]>
Pier Luca Lanzi talks at NIGEL 2006 about computed predictions]]> Mon, 04 May 2009 15:29:36 GMT /slideshow/computed-prediction-so-far-so-good-what-now/1384584 xllora@slideshare.net(xllora) Computed Prediction: So far, so good. What now? xllora Pier Luca Lanzi talks at NIGEL 2006 about computed predictions <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-lanzi-090504152951-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Pier Luca Lanzi talks at NIGEL 2006 about computed predictions

Computed Prediction: So far, so good. What now? from Xavier Llorà

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0 NIGEL 2006 welcome /slideshow/nigel-2006-welcome/1384574 nigel-2006-llora-welcome-090504152827-phpapp02
Welcome remarks by Xavier Llorà at the beginning of NIGEL 2006]]>
Welcome remarks by Xavier Llorà at the beginning of NIGEL 2006]]> Mon, 04 May 2009 15:28:16 GMT /slideshow/nigel-2006-welcome/1384574 xllora@slideshare.net(xllora) NIGEL 2006 welcome xllora Welcome remarks by Xavier Llorà at the beginning of NIGEL 2006 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-llora-welcome-090504152827-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Welcome remarks by Xavier Llorà at the beginning of NIGEL 2006

NIGEL 2006 welcome from Xavier Llorà

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0 Linkage Learning for Pittsburgh LCS: Making Problems Tractable /slideshow/linkage-learning-for-pittsburgh-lcs-making-problems-tractable/1384570 nigel-2006-llora-xeccs-090504152642-phpapp01
Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems]]>
Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems]]> Mon, 04 May 2009 15:26:38 GMT /slideshow/linkage-learning-for-pittsburgh-lcs-making-problems-tractable/1384570 xllora@slideshare.net(xllora) Linkage Learning for Pittsburgh LCS: Making Problems Tractable xllora Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nigel-2006-llora-xeccs-090504152642-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems

Linkage Learning for Pittsburgh LCS: Making Problems Tractable from Xavier Llorà

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0 Meandre: Semantic-Driven Data-Intensive Flows in the Clouds /slideshow/meandre-semanticdriven-dataintensive-flows-in-the-clout/1240498 meandre-into-cloud-cs-090402131332-phpapp02
A quick overview of the Meandre infrastructure, programming models and tools.]]>
A quick overview of the Meandre infrastructure, programming models and tools.]]> Thu, 02 Apr 2009 13:13:28 GMT /slideshow/meandre-semanticdriven-dataintensive-flows-in-the-clout/1240498 xllora@slideshare.net(xllora) Meandre: Semantic-Driven Data-Intensive Flows in the Clouds xllora A quick overview of the Meandre infrastructure, programming models and tools. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/meandre-into-cloud-cs-090402131332-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> A quick overview of the Meandre infrastructure, programming models and tools.

Meandre: Semantic-Driven Data-Intensive Flows in the Clouds from Xavier Llorà

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0 ZigZag: The Meandring Language /slideshow/zigzag-the-meandring-language/340722 zigzag-1207596448270805-9
This slideshow present a basic overview of Meandre's ZigZag scripting language]]>
This slideshow present a basic overview of Meandre's ZigZag scripting language]]> Mon, 07 Apr 2008 12:28:15 GMT /slideshow/zigzag-the-meandring-language/340722 xllora@slideshare.net(xllora) ZigZag: The Meandring Language xllora This slideshow present a basic overview of Meandre's ZigZag scripting language <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/zigzag-1207596448270805-9-thumbnail.jpg?width=120&height=120&fit=bounds" /> This slideshow present a basic overview of Meandre's ZigZag scripting language

ZigZag: The Meandring Language from Xavier Llorà

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0 HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infrared Spectroscopic Imaging /slideshow/humies-2007-bronze-winner-towards-better-than-human-capability-in-diagnosing-prostate-cancer-using-infrared-spectroscopic-imaging/79104 humies-2007-bronze-winner-towards-better-than-human-capability-in-diagnosing-prostate-cancer-using-infrared-spectroscopic-imaging4782
This slides where the ones presented during GECCO 2007 as part of the final process of the HUMIE awards. This work was awarded with the Bronze medal.]]>
This slides where the ones presented during GECCO 2007 as part of the final process of the HUMIE awards. This work was awarded with the Bronze medal.]]> Tue, 17 Jul 2007 20:34:05 GMT /slideshow/humies-2007-bronze-winner-towards-better-than-human-capability-in-diagnosing-prostate-cancer-using-infrared-spectroscopic-imaging/79104 xllora@slideshare.net(xllora) HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infrared Spectroscopic Imaging xllora This slides where the ones presented during GECCO 2007 as part of the final process of the HUMIE awards. This work was awarded with the Bronze medal. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/humies-2007-bronze-winner-towards-better-than-human-capability-in-diagnosing-prostate-cancer-using-infrared-spectroscopic-imaging4782-thumbnail.jpg?width=120&height=120&fit=bounds" /> This slides where the ones presented during GECCO 2007 as part of the final process of the HUMIE awards. This work was awarded with the Bronze medal.

HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infrared Spectroscopic Imaging from Xavier Llorà

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0 Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning Techniques /slideshow/do-not-match-inherit-fitness-surrogates-for-geneticsbased-machine-learning-techniques/79102 do-not-match-inherit-fitness-surrogates-for-geneticsbased-machine-learning-techniques4969
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.]]>
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.]]> Tue, 17 Jul 2007 20:28:48 GMT /slideshow/do-not-match-inherit-fitness-surrogates-for-geneticsbased-machine-learning-techniques/79102 xllora@slideshare.net(xllora) Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning Techniques xllora A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/do-not-match-inherit-fitness-surrogates-for-geneticsbased-machine-learning-techniques4969-thumbnail.jpg?width=120&height=120&fit=bounds" /> A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.

Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning Techniques from Xavier Llorà

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0 https://cdn.slidesharecdn.com/profile-photo-xllora-48x48.jpg?cb=1522780500 www.xavierllora.net https://cdn.slidesharecdn.com/ss_thumbnails/meandre2-0alpha-preview-100715140140-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/meandre-20-alpha-preview/4765637 Meandre 2.0 Alpha Preview https://cdn.slidesharecdn.com/ss_thumbnails/dir-workshop-20100315-100315174055-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/soaring-the-clouds-with-meandre/3440242 Soaring the Clouds wit... https://cdn.slidesharecdn.com/ss_thumbnails/monmouth-talk-slides-090918130141-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/from-galapagos-to-twitter-darwin-natural-selection-and-web-20/2019013 From Galapagos to Twit...