Computational Learning Theory - Info and Reading Options

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Lecture Notes in Artificial Intelligence -- Computational Learning Theory -- Copyright -- Preface -- Table of Contents -- 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 -- 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★

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