000 | 03241nam a22005535i 4500 | ||
---|---|---|---|
001 | 978-981-99-1790-7 | ||
003 | DE-He213 | ||
005 | 20240423130059.0 | ||
007 | cr nn 008mamaa | ||
008 | 230914s2023 si | s |||| 0|eng d | ||
020 |
_a9789819917907 _9978-981-99-1790-7 |
||
024 | 7 |
_a10.1007/978-981-99-1790-7 _2doi |
|
050 | 4 | _aQA76.9.D35 | |
050 | 4 | _aQ350-390 | |
072 | 7 |
_aUMB _2bicssc |
|
072 | 7 |
_aGPF _2bicssc |
|
072 | 7 |
_aCOM021000 _2bisacsh |
|
072 | 7 |
_aUMB _2thema |
|
072 | 7 |
_aGPF _2thema |
|
082 | 0 | 4 |
_a005.73 _223 |
082 | 0 | 4 |
_a003.54 _223 |
100 | 1 |
_aYamanishi, Kenji. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 0 |
_aLearning with the Minimum Description Length Principle _h[electronic resource] / _cby Kenji Yamanishi. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2023. |
|
300 |
_aXX, 339 p. 51 illus., 48 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 | _aInformation and Coding -- Parameter Estimation -- Model Selection -- Latent Variable Model Selection -- Sequential Prediction -- MDL Change Detection -- Continuous Model Selection -- Extension of Stochastic Complexity -- Mathematical Preliminaries. | |
520 | _aThis book introduces readers to the minimum description length (MDL) principle and its applications in learning. The MDL is a fundamental principle for inductive inference, which is used in many applications including statistical modeling, pattern recognition and machine learning. At its core, the MDL is based on the premise that “the shortest code length leads to the best strategy for learning anything from data.” The MDL provides a broad and unifying view of statistical inferences such as estimation, prediction and testing and, of course, machine learning. The content covers the theoretical foundations of the MDL and broad practical areas such as detecting changes and anomalies, problems involving latent variable models, and high dimensional statistical inference, among others. The book offers an easy-to-follow guide to the MDL principle, together with other information criteria, explaining the differences between their standpoints. Written in a systematic, concise and comprehensive style, this book is suitable for researchers and graduate students of machine learning, statistics, information theory and computer science. | ||
650 | 0 | _aData structures (Computer science). | |
650 | 0 | _aInformation theory. | |
650 | 0 | _aMachine learning. | |
650 | 1 | 4 | _aData Structures and Information Theory. |
650 | 2 | 4 | _aMachine Learning. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789819917891 |
776 | 0 | 8 |
_iPrinted edition: _z9789819917914 |
776 | 0 | 8 |
_iPrinted edition: _z9789819917921 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-99-1790-7 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c184831 _d184831 |