000 03778nam a22004575i 4500
001 978-3-642-20308-4
003 DE-He213
005 20140220083800.0
007 cr nn 008mamaa
008 110409s2011 gw | s |||| 0|eng d
020 _a9783642203084
_9978-3-642-20308-4
024 7 _a10.1007/978-3-642-20308-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aFreno, Antonino.
_eauthor.
245 1 0 _aHybrid Random Fields
_h[electronic resource] :
_bA Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models /
_cby Antonino Freno, Edmondo Trentin.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXVIII, 210 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v15
505 0 _aIntroduction -- Bayesian Networks -- Markov Random Fields -- Introducing Hybrid Random Fields: Discrete-Valued Variables -- Extending Hybrid Random Fields: Continuous-Valued Variables -- Applications -- Probabilistic Graphical Models: Cognitive Science or Cognitive Technology? . -- Conclusions.
520 _aThis book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it. -- Marco Gori, Università degli Studi di Siena Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aTrentin, Edmondo.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642203077
830 0 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v15
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-20308-4
912 _aZDB-2-ENG
999 _c107759
_d107759