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

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書目名稱Machine Learning Methods影響因子(影響力)




書目名稱Machine Learning Methods影響因子(影響力)學(xué)科排名




書目名稱Machine Learning Methods網(wǎng)絡(luò)公開度




書目名稱Machine Learning Methods網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning Methods被引頻次




書目名稱Machine Learning Methods被引頻次學(xué)科排名




書目名稱Machine Learning Methods年度引用




書目名稱Machine Learning Methods年度引用學(xué)科排名




書目名稱Machine Learning Methods讀者反饋




書目名稱Machine Learning Methods讀者反饋學(xué)科排名

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Hang LiProvides introduction to principle machine learning methods, covering both supervised and unsupervised learning methods.Presents clear descriptions, detailed proofs, and concrete examples using concis

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敏捷

Perceptron,This chapter first introduces the perceptron model, then describes the learning strategy of the perceptron, especially the loss function, and finally presents perceptron learning algorithms, including the primitive form and the dual form, and proves the algorithm’s convergence.

GONG

-Nearest Neighbor,This chapter first describes the .-NN algorithm, then discusses the model and three basic elements of .-NN, and finally describes an implementation method of .-NN—the .-tree, focusing on algorithms for constructing and searching the .-tree.

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ESPY

Decision Tree,This chapter first introduces the basic concept of the decision tree, then introduces feature selection, tree-generation and tree-pruning through ID3 and C4.5 algorithms, and finally introduces the CART algorithm.

Inveterate

Logistic Regression and Maximum Entropy Model,This chapter first introduces the logistic regression model, then the maximum entropy model, and finally describes the learning algorithms for logistic regression and maximum entropy models, including the improved iterative scaling algorithm and the Quasi-Newton method.

CRASS