�ݺ�ߣshows by User: nep_test

�ݺ�ߣshows by User: nep_test_account / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: nep_test_account / Fri, 18 Jan 2013 00:27:00 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: nep_test_account Database slide /slideshow/database-slide/16051143 database-slide2301-130118002701-phpapp01
Database slide]]>
Database slide]]> Fri, 18 Jan 2013 00:27:00 GMT /slideshow/database-slide/16051143 nep_test_account@slideshare.net(nep_test_account) Database slide nep_test_account Database slide <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/database-slide2301-130118002701-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Database slide

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0 database slide 1 /slideshow/database-slide-1/16050815 database-slide-12480-130124013412-phpapp02
database slide 1]]>
database slide 1]]> Thu, 17 Jan 2013 23:55:13 GMT /slideshow/database-slide-1/16050815 nep_test_account@slideshare.net(nep_test_account) database slide 1 nep_test_account database slide 1 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/database-slide-12480-130124013412-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> database slide 1

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0 Doc2pages /slideshow/doc2pages-16050671/16050671 doc2pages-130117233907-phpapp01
]]>
]]> Thu, 17 Jan 2013 23:39:07 GMT /slideshow/doc2pages-16050671/16050671 nep_test_account@slideshare.net(nep_test_account) Doc2pages nep_test_account <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/doc2pages-130117233907-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

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0 Doc2pages /slideshow/doc2pages/16050664 doc2pages618-130124013411-phpapp01
Doc2pages]]>
Doc2pages]]> Thu, 17 Jan 2013 23:38:45 GMT /slideshow/doc2pages/16050664 nep_test_account@slideshare.net(nep_test_account) Doc2pages nep_test_account Doc2pages <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/doc2pages618-130124013411-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Doc2pages

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0 PDF TEST /slideshow/pdf-test-16050577/16050577 pdf-test1887-130124013412-phpapp01
TEST]]>
TEST]]> Thu, 17 Jan 2013 23:26:31 GMT /slideshow/pdf-test-16050577/16050577 nep_test_account@slideshare.net(nep_test_account) PDF TEST nep_test_account TEST <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pdf-test1887-130124013412-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> TEST

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0 Baboom! /slideshow/baboom-16050555/16050555 baboom2716-130124013411-phpapp02
Boomba cha!]]>
Boomba cha!]]> Thu, 17 Jan 2013 23:23:13 GMT /slideshow/baboom-16050555/16050555 nep_test_account@slideshare.net(nep_test_account) Baboom! nep_test_account Boomba cha! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/baboom2716-130124013411-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Boomba cha!

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0 Baboom! /slideshow/baboom/16050554 baboom4055-130124013411-phpapp02
Boomba cha!]]>
Boomba cha!]]> Thu, 17 Jan 2013 23:23:13 GMT /slideshow/baboom/16050554 nep_test_account@slideshare.net(nep_test_account) Baboom! nep_test_account Boomba cha! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/baboom4055-130124013411-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Boomba cha!

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0 uat /slideshow/uat-16047465/16047465 uat4436-130124013410-phpapp02
test]]>
test]]> Thu, 17 Jan 2013 17:40:09 GMT /slideshow/uat-16047465/16047465 nep_test_account@slideshare.net(nep_test_account) uat nep_test_account test <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/uat4436-130124013410-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> test

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0 test pdf /slideshow/test-pdf-16040287/16040287 test-pdf3726-130117082533-phpapp01
pdf pdf]]>
pdf pdf]]> Thu, 17 Jan 2013 08:25:32 GMT /slideshow/test-pdf-16040287/16040287 nep_test_account@slideshare.net(nep_test_account) test pdf nep_test_account pdf pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/test-pdf3726-130117082533-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> pdf pdf

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0 08 linear classification_2 /slideshow/08-linear-classification2/14711082 08linearclassification2-121013055230-phpapp01
08 linear classification_2]]>
08 linear classification_2]]> Sat, 13 Oct 2012 05:52:29 GMT /slideshow/08-linear-classification2/14711082 nep_test_account@slideshare.net(nep_test_account) 08 linear classification_2 nep_test_account 08 linear classification_2 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/08linearclassification2-121013055230-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> 08 linear classification_2

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0 linear classification /slideshow/linear-classification-14623665/14623665 linear-classification4481-121007110641-phpapp01
linear classification]]>
linear classification]]> Sun, 07 Oct 2012 10:51:04 GMT /slideshow/linear-classification-14623665/14623665 nep_test_account@slideshare.net(nep_test_account) linear classification nep_test_account linear classification <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/linear-classification4481-121007110641-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> linear classification

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0 Lecture Notes in Machine Learning /slideshow/lecture-notes-in-machine-learning/14533132 lecture-notes-in-machine-learning380-120930233422-phpapp02
Any change in a system that allows it to perform better the second time on repetition of the same task or on another task drawn from the same population (Simon, 1983).]]>
Any change in a system that allows it to perform better the second time on repetition of the same task or on another task drawn from the same population (Simon, 1983).]]> Sun, 30 Sep 2012 23:15:12 GMT /slideshow/lecture-notes-in-machine-learning/14533132 nep_test_account@slideshare.net(nep_test_account) Lecture Notes in Machine Learning nep_test_account Any change in a system that allows it to perform better the second time on repetition of the same task or on another task drawn from the same population (Simon, 1983). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lecture-notes-in-machine-learning380-120930233422-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Any change in a system that allows it to perform better the second time on repetition of the same task or on another task drawn from the same population (Simon, 1983).

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0 Induction of Decision Trees /slideshow/induction-of-decision-trees/14533054 induction-of-decision-trees4436-120930231415-phpapp02
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.]]>
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.]]> Sun, 30 Sep 2012 23:08:30 GMT /slideshow/induction-of-decision-trees/14533054 nep_test_account@slideshare.net(nep_test_account) Induction of Decision Trees nep_test_account The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/induction-of-decision-trees4436-120930231415-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.

Induction of Decision Trees from nep_test_account

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0 Large-Scale Machine Learning at Twitter /slideshow/largescale-machine-learning-at-twitter/14532972 largescale-machine-learning-at-twitter2614-120930231419-phpapp01
The success of data-driven solutions to dicult problems, along with the dropping costs of storing and processing mas- sive amounts of data, has led to growing interest in large- scale machine learning. This paper presents a case study of Twitter's integration of machine learning tools into its existing Hadoop-based, Pig-centric analytics platform. We begin with an overview of this platform, which handles \tra- ditional" data warehousing and business intelligence tasks for the organization. The core of this work lies in recent Pig extensions to provide predictive analytics capabilities that incorporate machine learning, focused specically on super- vised classication. In particular, we have identied stochas- tic gradient descent techniques for online learning and en- semble methods as being highly amenable to scaling out to large amounts of data. In our deployed solution, common machine learning tasks such as data sampling, feature gen- eration, training, and testing can be accomplished directly in Pig, via carefully crafted loaders, storage functions, and user-dened functions. This means that machine learning is just another Pig script, which allows seamless integration with existing infrastructure for data management, schedul- ing, and monitoring in a production environment, as well as access to rich libraries of user-dened functions and the materialized output of other scripts.]]>
The success of data-driven solutions to dicult problems, along with the dropping costs of storing and processing mas- sive amounts of data, has led to growing interest in large- scale machine learning. This paper presents a case study of Twitter's integration of machine learning tools into its existing Hadoop-based, Pig-centric analytics platform. We begin with an overview of this platform, which handles \tra- ditional" data warehousing and business intelligence tasks for the organization. The core of this work lies in recent Pig extensions to provide predictive analytics capabilities that incorporate machine learning, focused specically on super- vised classication. In particular, we have identied stochas- tic gradient descent techniques for online learning and en- semble methods as being highly amenable to scaling out to large amounts of data. In our deployed solution, common machine learning tasks such as data sampling, feature gen- eration, training, and testing can be accomplished directly in Pig, via carefully crafted loaders, storage functions, and user-dened functions. This means that machine learning is just another Pig script, which allows seamless integration with existing infrastructure for data management, schedul- ing, and monitoring in a production environment, as well as access to rich libraries of user-dened functions and the materialized output of other scripts.]]> Sun, 30 Sep 2012 22:57:36 GMT /slideshow/largescale-machine-learning-at-twitter/14532972 nep_test_account@slideshare.net(nep_test_account) Large-Scale Machine Learning at Twitter nep_test_account The success of data-driven solutions to di�cult problems, along with the dropping costs of storing and processing mas- sive amounts of data, has led to growing interest in large- scale machine learning. This paper presents a case study of Twitter's integration of machine learning tools into its existing Hadoop-based, Pig-centric analytics platform. We begin with an overview of this platform, which handles \tra- ditional" data warehousing and business intelligence tasks for the organization. The core of this work lies in recent Pig extensions to provide predictive analytics capabilities that incorporate machine learning, focused speci�cally on super- vised classi�cation. In particular, we have identi�ed stochas- tic gradient descent techniques for online learning and en- semble methods as being highly amenable to scaling out to large amounts of data. In our deployed solution, common machine learning tasks such as data sampling, feature gen- eration, training, and testing can be accomplished directly in Pig, via carefully crafted loaders, storage functions, and user-de�ned functions. This means that machine learning is just another Pig script, which allows seamless integration with existing infrastructure for data management, schedul- ing, and monitoring in a production environment, as well as access to rich libraries of user-de�ned functions and the materialized output of other scripts. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/largescale-machine-learning-at-twitter2614-120930231419-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The success of data-driven solutions to di�cult problems, along with the dropping costs of storing and processing mas- sive amounts of data, has led to growing interest in large- scale machine learning. This paper presents a case study of Twitter's integration of machine learning tools into its existing Hadoop-based, Pig-centric analytics platform. We begin with an overview of this platform, which handles \tra- ditional" data warehousing and business intelligence tasks for the organization. The core of this work lies in recent Pig extensions to provide predictive analytics capabilities that incorporate machine learning, focused speci�cally on super- vised classi�cation. In particular, we have identi�ed stochas- tic gradient descent techniques for online learning and en- semble methods as being highly amenable to scaling out to large amounts of data. In our deployed solution, common machine learning tasks such as data sampling, feature gen- eration, training, and testing can be accomplished directly in Pig, via carefully crafted loaders, storage functions, and user-de�ned functions. This means that machine learning is just another Pig script, which allows seamless integration with existing infrastructure for data management, schedul- ing, and monitoring in a production environment, as well as access to rich libraries of user-de�ned functions and the materialized output of other scripts.

Large-Scale Machine Learning at Twitter from nep_test_account

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0 A Few Useful Things to Know about Machine Learning /slideshow/a-few-useful-things-to-know-about-machine-learning/14532787 a-few-useful-things-to-know-about-machine-learning3187-120930225412-phpapp01
Machine learning algorithms can ﬁgure out how to perform important tasks by generalizing from examples. This is often feasible and cost-eﬀective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other ﬁelds. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to ﬁnd in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.]]>
Machine learning algorithms can ﬁgure out how to perform important tasks by generalizing from examples. This is often feasible and cost-eﬀective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other ﬁelds. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to ﬁnd in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.]]> Sun, 30 Sep 2012 22:32:26 GMT /slideshow/a-few-useful-things-to-know-about-machine-learning/14532787 nep_test_account@slideshare.net(nep_test_account) A Few Useful Things to Know about Machine Learning nep_test_account Machine learning algorithms can ﬁgure out how to perform important tasks by generalizing from examples. This is often feasible and cost-eﬀective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other ﬁelds. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to ﬁnd in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/a-few-useful-things-to-know-about-machine-learning3187-120930225412-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Machine learning algorithms can ﬁgure out how to perform important tasks by generalizing from examples. This is often feasible and cost-eﬀective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other ﬁelds. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to ﬁnd in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.

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0 linear regression part 2 /slideshow/linear-regression-part-2/14522888 linear-regression-part-21579-120930023926-phpapp02
part 2]]>
part 2]]> Sun, 30 Sep 2012 02:34:14 GMT /slideshow/linear-regression-part-2/14522888 nep_test_account@slideshare.net(nep_test_account) linear regression part 2 nep_test_account part 2 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/linear-regression-part-21579-120930023926-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> part 2

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0 Linear Regression /slideshow/linear-regression-14514238/14514238 linear-regression4566-120929062550-phpapp01
Linear Regression continuous value prediction.]]>
Linear Regression continuous value prediction.]]> Sat, 29 Sep 2012 06:20:35 GMT /slideshow/linear-regression-14514238/14514238 nep_test_account@slideshare.net(nep_test_account) Linear Regression nep_test_account Linear Regression continuous value prediction. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/linear-regression4566-120929062550-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Linear Regression continuous value prediction.

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0 Probability /slideshow/probability-14512838/14512838 probability1473-120929030309-phpapp02
Probability for Machine Learning]]>
Probability for Machine Learning]]> Sat, 29 Sep 2012 03:01:38 GMT /slideshow/probability-14512838/14512838 nep_test_account@slideshare.net(nep_test_account) Probability nep_test_account Probability for Machine Learning <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/probability1473-120929030309-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Probability for Machine Learning

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0 Linear Algebra /slideshow/linear-algebra/14512791 linear-algebra3497-120929031938-phpapp01
Linear Algebra revision for the SML]]>
Linear Algebra revision for the SML]]> Sat, 29 Sep 2012 02:57:39 GMT /slideshow/linear-algebra/14512791 nep_test_account@slideshare.net(nep_test_account) Linear Algebra nep_test_account Linear Algebra revision for the SML <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/linear-algebra3497-120929031938-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Linear Algebra revision for the SML

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0 Introduction /slideshow/introduction-14512466/14512466 introduction736-120929022034-phpapp02
Statistical Machine Learning from ANU University]]>
Statistical Machine Learning from ANU University]]> Sat, 29 Sep 2012 02:12:49 GMT /slideshow/introduction-14512466/14512466 nep_test_account@slideshare.net(nep_test_account) Introduction nep_test_account Statistical Machine Learning from ANU University <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introduction736-120929022034-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Statistical Machine Learning from ANU University

Introduction from nep_test_account

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