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020			_a9789819939176
024	7		_a10.1007/978-981-99-3917-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050	0	4	_aQ325.5-.7 _b2024
100	1		_aLi, Hang. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aMachine Learning Methods _cby Hang Li
250			_afirst edition 2024
264		1	_aSingapore _c2024 _bSpringer International Publishing
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
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.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-3917-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
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988			_aSpringer_Computer_2024
999			_c397776 _d397776