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001			978-981-99-3917-6
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020			_a9789819939176 _9978-981-99-3917-6
024	7		_a10.1007/978-981-99-3917-6 _2doi
050		4	_aQ325.5-.7
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072		7	_aMAT029000 _2bisacsh
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082	0	4	_a006.31 _223
100	1		_aLi, Hang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aMachine Learning Methods _h[electronic resource] / _cby Hang Li.
250			_a1st ed. 2024.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024.
300			_aXV, 532 p. 109 illus., 5 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChapter 1 Introduction to Machine learning and Supervised Learning -- Chapter 2 Perceptron -- Chapter 3 K-Nearest-Neighbor -- Chapter 4 The Naïve Bayes Method -- Chapter 5 Decision Tree -- Chapter 6 Logistic Regression and Maximum Entropy Model -- Chapter 7 Support Vector Machine -- Chapter 8 Boosting -- Chapter 9 EM Algorithm and Its Extensions -- Chapter 10 Hidden Markov Model -- Chapter 11 Conditional Random Field.
520			_aThis book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.
650		0	_aMachine learning.
650	1	4	_aMachine Learning.
650	2	4	_aStatistical Learning.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789819939169
776	0	8	_iPrinted edition: _z9789819939183
776	0	8	_iPrinted edition: _z9789819939190
856	4	0	_uhttps://doi.org/10.1007/978-981-99-3917-6
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c186522 _d186522