000			03727nam a22005655i 4500
001			978-3-030-65900-4
003			DE-He213
005			20220801220228.0
007			cr nn 008mamaa
008			210212s2021 sz | s |||| 0|eng d
020			_a9783030659004 _9978-3-030-65900-4
024	7		_a10.1007/978-3-030-65900-4 _2doi
050		4	_aTK5101-5105.9
072		7	_aTJK _2bicssc
072		7	_aTEC041000 _2bisacsh
072		7	_aTJK _2thema
082	0	4	_a621.382 _223
100	1		_aJo, Taeho. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _949055
245	1	0	_aMachine Learning Foundations _h[electronic resource] : _bSupervised, Unsupervised, and Advanced Learning / _cby Taeho Jo.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_aXX, 391 p. 277 illus., 13 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		_aPart I. Foundation -- Chapter 1. Introduction -- Chapter 2. Numerical Vectors -- Chapter 3.Data Encoding -- Chapter 4. Simple Machine Learning Algorithms -- Part II. Supervised Learning -- Chapter 5. Instance based Learning -- Chapter 6. Probabilistic Learning -- Chapter 7. Decision Tree -- Chapter 8. Support Vector Machine -- Part III. Unsupervised Learning -- Chapter 9. Simple Clustering Algorithms -- Chapter 10. K Means Algorithm -- Chapter 11. EM Algorithm -- Chapter 12. Advanced Clustering -- Part IV. Advanced Topics -- Chapter 13. Ensemble Learning -- Chapter 14. Semi-Supervised Learning -- Chapter 15. Temporal Learning -- Chapter 16. Reinforcement Learning.
520			_aThis book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
650		0	_aTelecommunication. _910437
650		0	_aComputational intelligence. _97716
650		0	_aData mining. _93907
650		0	_aInformation storage and retrieval systems. _922213
650		0	_aQuantitative research. _94633
650	1	4	_aCommunications Engineering, Networks. _931570
650	2	4	_aComputational Intelligence. _97716
650	2	4	_aData Mining and Knowledge Discovery. _949056
650	2	4	_aInformation Storage and Retrieval. _923927
650	2	4	_aData Analysis and Big Data. _949057
710	2		_aSpringerLink (Online service) _949058
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030658991
776	0	8	_iPrinted edition: _z9783030659011
776	0	8	_iPrinted edition: _z9783030659028
856	4	0	_uhttps://doi.org/10.1007/978-3-030-65900-4
912			_aZDB-2-ENG
912			_aZDB-2-SXE
942			_cEBK
999			_c78342 _d78342