|
|
|
|
| LEADER |
00000nam a22000005i 4500 |
| 001 |
978-3-540-35296-9 |
| 003 |
DE-He213 |
| 005 |
20221012202555.0 |
| 007 |
cr nn 008mamaa |
| 008 |
100301s2006 gw | s |||| 0|eng d |
| 020 |
|
|
|a 9783540352969
|9 978-3-540-35296-9
|
| 024 |
7 |
|
|a 10.1007/11776420
|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 Learning Theory
|h [electronic resource] :
|b 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings /
|c edited by Hans Ulrich Simon, Gábor Lugosi.
|
| 250 |
|
|
|a 1st ed. 2006.
|
| 264 |
|
1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2006.
|
| 300 |
|
|
|a XII, 660 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 4005
|
| 505 |
0 |
|
|a Invited Presentations -- Random Multivariate Search Trees -- On Learning and Logic -- Predictions as Statements and Decisions -- Clustering, Un-, and Semisupervised Learning -- A Sober Look at Clustering Stability -- PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption -- Stable Transductive Learning -- Uniform Convergence of Adaptive Graph-Based Regularization -- Statistical Learning Theory -- The Rademacher Complexity of Linear Transformation Classes -- Function Classes That Approximate the Bayes Risk -- Functional Classification with Margin Conditions -- Significance and Recovery of Block Structures in Binary Matrices with Noise -- Regularized Learning and Kernel Methods -- Maximum Entropy Distribution Estimation with Generalized Regularization -- Unifying Divergence Minimization and Statistical Inference Via Convex Duality -- Mercer's Theorem, Feature Maps, and Smoothing -- Learning Bounds for Support Vector Machines with Learned Kernels -- Query Learning and Teaching -- On Optimal Learning Algorithms for Multiplicity Automata -- Exact Learning Composed Classes with a Small Number of Mistakes -- DNF Are Teachable in the Average Case -- Teaching Randomized Learners -- Inductive Inference -- Memory-Limited U-Shaped Learning -- On Learning Languages from Positive Data and a Limited Number of Short Counterexamples -- Learning Rational Stochastic Languages -- Parent Assignment Is Hard for the MDL, AIC, and NML Costs -- Learning Algorithms and Limitations on Learning -- Uniform-Distribution Learnability of Noisy Linear Threshold Functions with Restricted Focus of Attention -- Discriminative Learning Can Succeed Where Generative Learning Fails -- Improved Lower Bounds for Learning Intersections of Halfspaces -- Efficient Learning Algorithms Yield Circuit Lower Bounds -- Online Aggregation -- Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition -- Aggregation and Sparsity Via ?1 Penalized Least Squares -- A Randomized Online Learning Algorithm for Better Variance Control -- Online Prediction and Reinforcement Learning I -- Online Learning with Variable Stage Duration -- Online Learning Meets Optimization in the Dual -- Online Tracking of Linear Subspaces -- Online Multitask Learning -- Online Prediction and Reinforcement Learning II -- The Shortest Path Problem Under Partial Monitoring -- Tracking the Best Hyperplane with a Simple Budget Perceptron -- Logarithmic Regret Algorithms for Online Convex Optimization -- Online Variance Minimization -- Online Prediction and Reinforcement Learning III -- Online Learning with Constraints -- Continuous Experts and the Binning Algorithm -- Competing with Wild Prediction Rules -- Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path -- Other Approaches -- Ranking with a P-Norm Push -- Subset Ranking Using Regression -- Active Sampling for Multiple Output Identification -- Improving Random Projections Using Marginal Information -- Open Problems -- Efficient Algorithms for General Active Learning -- Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a Computer science.
|
| 650 |
|
0 |
|a Algorithms.
|
| 650 |
|
0 |
|a Machine theory.
|
| 650 |
1 |
4 |
|a Artificial Intelligence.
|
| 650 |
2 |
4 |
|a Theory of Computation.
|
| 650 |
2 |
4 |
|a Algorithms.
|
| 650 |
2 |
4 |
|a Formal Languages and Automata Theory.
|
| 700 |
1 |
|
|a Simon, Hans Ulrich.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 700 |
1 |
|
|a Lugosi, Gábor.
|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 9783540825562
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783540352945
|
| 830 |
|
0 |
|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 4005
|
| 856 |
4 |
0 |
|u https://doi.uam.elogim.com/10.1007/11776420
|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)
|
