| The LNAI series reports state-of-the-art results in artificial intelligence re-search，development， and education， at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community， with numerous individuals， as well as with prestigious organizations and societies，LNAI has grown into the most comprehensive artificial intelligence research forum available. The scope of LNAI spans the whole range of artificial intelligence and intelli-gent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes. —proceedings （published in time for the respective conference） —post-proceedings （consisting of thoroughly revised final full papers） —research monographs（which may be based on PhD work）. |
| Statistical Learning Theory Agnostic Learning Nonconvex Function Classes Entropy, Combinatorial Dimensions and Random Averages Geometric Parameters of Kernel Machines Localized Rademacher Complexities Some Local Measures of Complexity of Convex Hulls and Generalization Bounds Online Learning Path Kernels and Multiplicative Updates Predictive Complexity and Information Mixability and the Existence of Weak Complexities A Second-Order Perceptron Algorithm Tracking Linear-Threshold Concepts with Winnow Inductive Inference Learning Tree Languages from Text Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data Inferring Deterministic Linear Languages Merging Uniform Inductive Learners The Speed Prior: A New Simplicity Measure PAC Learning New Lower Bounds for Statistical Query Learning Exploring Learnability between Exact and PAC PAC Bounds for Multi-armed Bandit and Markov Decision Processes Bounds for the Minimum Disagreement Problem with Applications to Learning Theory On the Proper Learning of Axis Parallel Concepts Boosting A Consistent Strategy for Boosting Algorithms The Consistency of Greedy Algorithms for Classification Maximizing the Margin with Boosting Other Learning Paradigms Performance Guarantees for Hierarchical Clustering Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures Prediction and Dimension Invited Talk Author Index |
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