Titlebook: Machine Learning Methods; Hang Li Textbook 2024 Tsinghua University Press 2024 Machine Learning.Statistical Learning.Supervised Learning.U

百科全書

Hidden Markov Model,This chapter first introduces the basic concepts of the Hidden Markov Model, and then describes the probability calculation algorithms, learning algorithms, and prediction algorithms of the HMM, respectively.

預(yù)定

Jogging

Summary of Supervised Learning Methods,This chapter summarizes the characteristics of ten supervised learning methods, including the perceptron, .-Nearest-Neighbor (.-NN), the Na?ve Bayes method, the decision tree, logistic regression and maximum entropy model, Support Vector Machine (SVM), Boosting, the EM algorithm, Hidden Markov Model (HMM), and Conditional random field (CRF).

變白

mitten

thyroid-hormone

Singular Value Decomposition,This chapter introduces the matrix factorization method—Singular Value Decomposition (SVD), including the definition and fundamental theorem of matrix SVD, its compact and truncated form, geometric interpretation, and main properties, its optimization algorithms.

SHRIK

Evolve

Latent Semantic Analysis,This chapter introduces an unsupervised learning method—Latent Semantic Analysis (LSA), first describing the word vector space model and the topic vector space model, followed by the SVD algorithm for LSA, and the Non-negative matrix factorization (NMF) algorithm.

流動性

erythema