Murpy's Machine Learning 9. Generalize Linear Model
- 1. Machine Learning Logistic Regression Generalized Linear Model
- 2. 8.1 Introduction, overview 8.2 Model specification 8.3 Model fitting 8.3.1 MLE 8.3.2 Steepest descent 8.3.3 Newton's method 8.3.6 l2 regularization 8.3.7 Multi-class logistic regression 8.4 Bayesian logistic regression 8.4.1 Laplace approximation 8.4.2 Derivation of the BIC(Bayesian Information Criterion) 8.4.3 Gaussian approximation for logistic regression 8.4.4 Approximating the posterior predictive 8.5 Online learning and stochastic optimization 8.5.3 The LMS algorithm 8.5.4 The perceptron algorithm 8.5.5 A Bayesian view 8.6 Generative vs discriminative classifiers 8.6.1 Pros and cons of each approach
- 5. るジ 豕 蠍磯(蟆曙 螳, 危,) 伎 豕
- 6. 豸′-れ豺
- 8. 伎 覩碁
- 16. 蠏覿殊 蠏碁 蠏願 蠏 MAP豢豺 朱襦 l2 reg 螳讌
- 21. 9.1 Introduction 9.2 The exponential family 9.2.1 Definition 9.2.2.1 Bernoulli 9.2.2.2 Multinoulli 9.2.2.3 Univariate Gaussian 9.2.3 Log partition function 9.2.3.1 Example: the Bernoulli distribution 9.3 Generalized linear models (GLMs) 9.3.1 Basics 9.3.2 ML and MAP estimation 9.3.3 Bayesian inference
- 23. Normalize term 企 覿襯 讌 螳 覈朱 朱 讌譟煙企手 . 譟
- 29. Likelihood 豢覿糾
- 30. 豌 覃伎/豌
- 35. logistic regression 蟆曙 亮 = 1/(1+exp(-w'x)) 企襦 S 轟 8.3.1 蟆郁骸 螳讌. Logistic R gradient 覿瑚 覦蟇 蟆郁骸 NLL 伎 蟇磯