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999			_c398019 _d398019 _x1
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008			230120s2023 si | o |||| 0|eng d
020			_a9789811979088
024	7		_a10.1007/978-981-19-7908-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2023 EB
100	1		_aMurty, M. Narasimha _eautor _4http://id.loc.gov/vocabulary/relators/aut _999777
245	1	0	_aRepresentation in Machine Learning _cby M. N. Murty, M. Avinash
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_a1. Introduction -- 2. Representation -- 3. Nearest Neighbor Algorithms -- 4. Representation Using Linear Combinations -- 5. Non-Linear Schemes for Representation -- 6. Conclusions.
520			_aThis book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book. In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques' effectiveness.
988			_aSpringer_Computer_2023
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_9689486 _aAvinash, M. _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-7908-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI