LEADER 01145nam0 2200313 450 001 000033766 005 20130805110250.0 100 $a20121107d1978----km-y0itaa50------ba 101 0 $aita 102 $aIT 200 1 $aPsicoanalisi linguistica ed epistemologia in Jacques Lacan$fMario Francioni 210 $aTorino$cBoringhieri$d1978 215 $a81 p.$d21 cm 225 2 $aLezioni e seminari 410 0$12001$aLezioni e seminari 600 1$aLacan,$bJacques 676 $a616.8917$v(21. ed.)$9Disturbi psichici. Psicanalisi 700 1$aFrancioni,$bMario$0156667 801 0$aIT$bUniversità della Basilicata - B.I.A.$gREICAT$2unimarc 912 $a000033766 996 $aPsicoanalisi linguistica ed epistemologia in Jacques Lacan$996724 997 $aUNIBAS BAS $aLETTERE CAT $aSTD081$b01$c20121107$lBAS01$h0959 CAT $aSTD081$b01$c20121107$lBAS01$h1114 CAT $aSTD091$b01$c20130610$lBAS01$h1224 CAT $aMDL$b30$c20130805$lBAS01$h1102 FMT Z30 -1$lBAS01$LBAS01$mBOOK$1BASA1$APolo Storico-Umanistico$2GEN$BCollezione generale$3FP/54604$654604$5L54604$820121107$f02$FPrestabile Generale LEADER 05104nam 22008175 450 001 9910484524603321 005 20251226202945.0 010 $a3-540-33428-9 024 7 $a10.1007/11736790 035 $a(CKB)1000000000232927 035 $a(SSID)ssj0000318735 035 $a(PQKBManifestationID)11240010 035 $a(PQKBTitleCode)TC0000318735 035 $a(PQKBWorkID)10311626 035 $a(PQKB)10452653 035 $a(DE-He213)978-3-540-33428-6 035 $a(MiAaPQ)EBC3067600 035 $a(PPN)123133548 035 $a(BIP)34164024 035 $a(BIP)13336740 035 $a(EXLCZ)991000000000232927 100 $a20100301d2006 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning Challenges $eEvaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers /$fedited by Joaquin Quinonero-Candela, Ido Dagan, Bernardo Magnini, Florence d'Alché-Buc 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (XIII, 462 p.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3944 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-33427-0 320 $aIncludes bibliographical references and index. 327 $aEvaluating Predictive Uncertainty Challenge -- Classification with Bayesian Neural Networks -- A Pragmatic Bayesian Approach to Predictive Uncertainty -- Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees -- Estimating Predictive Variances with Kernel Ridge Regression -- Competitive Associative Nets and Cross-Validation for Estimating Predictive Uncertainty on Regression Problems -- Lessons Learned in the Challenge: Making Predictions and Scoring Them -- The 2005 PASCAL Visual Object Classes Challenge -- The PASCAL Recognising Textual Entailment Challenge -- Using Bleu-like Algorithms for the Automatic Recognition of Entailment -- What Syntax Can Contribute in the Entailment Task -- Combining Lexical Resources with Tree Edit Distance for Recognizing Textual Entailment -- Textual Entailment Recognition Based on Dependency Analysis and WordNet -- Learning Textual Entailment on a Distance Feature Space -- An Inference Model for Semantic Entailment in Natural Language -- A Lexical Alignment Model for Probabilistic Textual Entailment -- Textual Entailment Recognition Using Inversion Transduction Grammars -- Evaluating Semantic Evaluations: How RTE Measures Up -- Partial Predicate Argument Structure Matching for Entailment Determination -- VENSES ? A Linguistically-Based System for Semantic Evaluation -- Textual Entailment Recognition Using a Linguistically?Motivated Decision Tree Classifier -- Recognizing Textual Entailment Via Atomic Propositions -- Recognising Textual Entailment with Robust Logical Inference -- Applying COGEX to Recognize Textual Entailment -- Recognizing Textual Entailment: Is Word Similarity Enough?. 330 $aThis book constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v3944 606 $aArtificial intelligence 606 $aAlgorithms 606 $aMachine theory 606 $aNatural language processing (Computer science) 606 $aComputer vision 606 $aPattern recognition systems 606 $aArtificial Intelligence 606 $aAlgorithms 606 $aFormal Languages and Automata Theory 606 $aNatural Language Processing (NLP) 606 $aComputer Vision 606 $aAutomated Pattern Recognition 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aMachine theory. 615 0$aNatural language processing (Computer science). 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 14$aArtificial Intelligence. 615 24$aAlgorithms. 615 24$aFormal Languages and Automata Theory. 615 24$aNatural Language Processing (NLP). 615 24$aComputer Vision. 615 24$aAutomated Pattern Recognition. 676 $a006.3/1 701 $aQuinonero-Candela$b Joaquin$0518515 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484524603321 996 $aMachine learning challenges$94184373 997 $aUNINA