|
|
|
|
| LEADER |
00000nam a22000005i 4500 |
| 001 |
978-3-540-33428-6 |
| 003 |
DE-He213 |
| 005 |
20221012194654.0 |
| 007 |
cr nn 008mamaa |
| 008 |
100301s2006 gw | s |||| 0|eng d |
| 020 |
|
|
|a 9783540334286
|9 978-3-540-33428-6
|
| 024 |
7 |
|
|a 10.1007/11736790
|2 doi
|
| 050 |
|
4 |
|a Q334-342
|
| 050 |
|
4 |
|a TA347.A78
|
| 072 |
|
7 |
|a UYQ
|2 bicssc
|
| 072 |
|
7 |
|a COM004000
|2 bisacsh
|
| 072 |
|
7 |
|a UYQ
|2 thema
|
| 082 |
0 |
4 |
|a 006.3
|2 23
|
| 245 |
1 |
0 |
|a Machine Learning Challenges
|h [electronic resource] :
|b Evaluating 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 /
|c edited by Joaquin Quinonero-Candela, Ido Dagan, Bernardo Magnini, Florence d'Alché-Buc.
|
| 250 |
|
|
|a 1st ed. 2006.
|
| 264 |
|
1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2006.
|
| 300 |
|
|
|a XIII, 462 p.
|b online resource.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 347 |
|
|
|a text file
|b PDF
|2 rda
|
| 490 |
1 |
|
|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 3944
|
| 505 |
0 |
|
|a Evaluating 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?.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a Algorithms.
|
| 650 |
|
0 |
|a Machine theory.
|
| 650 |
|
0 |
|a Natural language processing (Computer science).
|
| 650 |
|
0 |
|a Computer vision.
|
| 650 |
|
0 |
|a Pattern recognition systems.
|
| 650 |
1 |
4 |
|a Artificial Intelligence.
|
| 650 |
2 |
4 |
|a Algorithms.
|
| 650 |
2 |
4 |
|a Formal Languages and Automata Theory.
|
| 650 |
2 |
4 |
|a Natural Language Processing (NLP).
|
| 650 |
2 |
4 |
|a Computer Vision.
|
| 650 |
2 |
4 |
|a Automated Pattern Recognition.
|
| 700 |
1 |
|
|a Quinonero-Candela, Joaquin.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 700 |
1 |
|
|a Dagan, Ido.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 700 |
1 |
|
|a Magnini, Bernardo.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 700 |
1 |
|
|a d'Alché-Buc, Florence.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer Nature eBook
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783540822752
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783540334279
|
| 830 |
|
0 |
|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 3944
|
| 856 |
4 |
0 |
|u https://doi.uam.elogim.com/10.1007/11736790
|z Texto Completo
|
| 912 |
|
|
|a ZDB-2-SCS
|
| 912 |
|
|
|a ZDB-2-SXCS
|
| 912 |
|
|
|a ZDB-2-LNC
|
| 950 |
|
|
|a Computer Science (SpringerNature-11645)
|
| 950 |
|
|
|a Computer Science (R0) (SpringerNature-43710)
|
