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001 978-3-319-94989-5
003 DE-He213
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008 180801s2018 gw | s |||| 0|eng d
020 _a9783319949895
_9978-3-319-94989-5
024 7 _a10.1007/978-3-319-94989-5
_2doi
040 _cCUS
050 4 _aQ334-342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aF MELLO, RODRIGO.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMachine Learning
_h[electronic resource] :
_bA Practical Approach on the Statistical Learning Theory /
_cby RODRIGO F MELLO, Moacir Antonelli Ponti.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXV, 362 p. 190 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1 – A Brief Review on Machine Learning -- Chapter 2 - Statistical Learning Theory -- Chapter 3 - Assessing Learning Algorithms -- Chapter 4 - Introduction to Support Vector Machines -- Chapter 5 - In Search for the Optimization Algorithm -- Chapter 6 - A Brief Introduction on Kernels -- .
520 _aThis book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
650 0 _aArtificial intelligence.
650 0 _aMathematical statistics.
650 0 _aComputer science—Mathematics.
650 0 _aComputer mathematics.
650 0 _aStatistics .
650 1 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650 2 4 _aProbability and Statistics in Computer Science.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I17036
650 2 4 _aMathematical Applications in Computer Science.
_0https://scigraph.springernature.com/ontologies/product-market-codes/M13110
650 2 4 _aApplied Statistics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/S17000
700 1 _aAntonelli Ponti, Moacir.
856 4 0 _uhttps://doi.org/10.1007/978-3-319-94989-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cEBK
999 _c204352
_d204352