Computational learning theory : 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 16 19, 2001 : proceedings
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| Médium: | Livre numérique |
| Jazyk: | Anglais |
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| Edice: | Lecture notes in computer science. Lecture notes in artificial intelligence
2111 |
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| Edition sous un autre format: | • Computational learning theory, 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 16-19, 2001, proceedings, David Helmbold, Bob Williamson (Eds.), 2001, Berlin, Springer, 1 vol. (IX-629 p.), Lecture notes in computer science, 3-540-42343-5 • Computational Learning Theory, Texte imprimé, 9783662214053 |
Obsah:
- How Many Queries Are Needed to Learn One Bit of Information?
- Radial Basis Function Neural Networks Have Superlinear VC Dimension
- Tracking a Small Set of Experts by Mixing Past Posteriors
- Potential-Based Algorithms in Online Prediction and Game Theory
- A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning
- Efficiently Approximating Weighted Sums with Exponentially Many Terms
- Ultraconservative Online Algorithms for Multiclass Problems
- Estimating a Boolean Perceptron from Its Average Satisfying Assignment: A Bound on the Precision Required
- Adaptive Strategies and Regret Minimization in Arbitrarily Varying Markov Environments
- Robust Learning Rich and Poor
- On the Synthesis of Strategies Identifying Recursive Functions
- Intrinsic Complexity of Learning Geometrical Concepts from Positive Data
- Toward a Computational Theory of Data Acquisition and Truthing
- Discrete Prediction Games with Arbitrary Feedback and Loss (Extended Abstract)
- Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
- Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights
- Geometric Methods in the Analysis of Glivenko-Cantelli Classes
- Learning Relatively Small Classes
- On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses
- When Can Two Unsupervised Learners Achieve PAC Separation?
- Strong Entropy Concentration, Game Theory, and Algorithmic Randomness
- Pattern Recognition and Density Estimation under the General i.i.d. Assumption
- A General Dimension for Exact Learning
- Data-Dependent Margin-Based Generalization Bounds for Classification
- Limitations of Learning via Embeddings in Euclidean Half-Spaces
- Estimating the OptimalMargins of Embeddings in Euclidean Half Spaces
- A Generalized Representer Theorem
- A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning
- Learning Additive Models Online with Fast Evaluating Kernels
- Geometric Bounds for Generalization in Boosting
- Smooth Boosting and Learning with Malicious Noise
- On Boosting with Optimal Poly-Bounded Distributions
- Agnostic Boosting
- A Theoretical Analysis of Query Selection for Collaborative Filtering
- On Using Extended Statistical Queries to Avoid Membership Queries
- Learning Monotone DNF from a Teacher That Almost Does Not Answer Membership Queries
- On Learning Monotone DNF under Product Distributions
- Learning Regular Sets with an Incomplete Membership Oracle
- Learning Rates for Q-Learning
- Optimizing Average Reward Using Discounted Rewards
- Bounds on Sample Size for Policy Evaluation in Markov Environments.