Machine learning and data mining
Problems:
Classification, Clustering, Regression, Anomaly detection, Association rules,
Reinforcement learning, Structurd prediction, Feature learning, Online learning,
Semi-supervised learning, Grammar induction
Supervised learning:
Decision trees, Ensembles(Bagging, Boostring, Random forest), k-MN, Linear regression,
Native Bayes, Nenural networks, Logistic regression, Perceptron,
Support vector machine(SVM), Relevance vector machine(RVM)
Clustering:
BIRCH, Hierachical, K-means, Expectation-maximization(EM), DBSCAN, OPTICS, Mean-shift
Dimensionality reduction:
Factor analysis, CCA, ICA, LDA, NMF, PCA, t-SNE
Structured prediction:
Graphical models(Bayes net, CRF, HMM)
Anomaly detection:
k-MN, Local outlier factor
Neural nets:
Autoencoder, Deep learning, Multiayer perceptron, RNN, Restricted Boltzmann machine,
SOM, Convolutional neural network
Theory
Bias-variance dilemma, Computational learnig theory, Empirical risk minimization,
PAC learning, Statistical learning, VC theory
本文出自 “Koala程序员” 博客，请务必保留此出处http://koala87.blog.51cto.com/8339141/1637785
郑重声明：本站内容如果来自互联网及其他传播媒体，其版权均属原媒体及文章作者所有。转载目的在于传递更多信息及用于网络分享，并不代表本站赞同其观点和对其真实性负责，也不构成任何其他建议。
