Machine learning and data mining

标签：data mining   machine learning   

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

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Machine learning and data mining

标签：data mining   machine learning   

原文地址：http://koala87.blog.51cto.com/8339141/1637785