Titlebook: Learning Theory; 19th Annual Conferen Gábor Lugosi,Hans Ulrich Simon Conference proceedings 2006 Springer-Verlag Berlin Heidelberg 2006 Clu

Influx

Random Multivariate Search Treesr kd-trees, quadtrees, BSP trees, simplex trees, grid trees, epsilon nets, and many other structures. The height of these trees is logarithmic in the data size for random input. Some search operations such as range search and nearest neighbor search have surprising complexities. So, we will give a b

災(zāi)禍

死貓他燒焦

用肘

PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumptions introduced by Kearns?et?al. [13]. Here the task is to construct a hypothesis mixture of Gaussians that is statistically indistinguishable from the actual mixture generating the data; specifically, the KL?divergence should be at most ...In this scenario, we give a poly(./.) time algorithm that lear

新娘

Stable Transductive Learningres the sensitivity of the algorithm to most pairwise exchanges of training and test set points. Our bound is based on a novel concentration inequality for symmetric functions of permutations. We also present a simple sampling technique that can estimate, with high probability, the weak stability of

Irksome

Tempor

abracadabra

Functional Classification with Margin Conditionsthis sample a classifier that is a function which would predict the value of . from the observation of .. The special case where . is a functional space is of particular interest due to the so called .. In a recent paper, Biau . [1] propose to filter the ..’s in the Fourier basis and to apply the cl

虛構(gòu)的東西

Madrigal

Maximum Entropy Distribution Estimation with Generalized Regularizationor, alternatively, by convex regularization. We provide fully general performance guarantees and an algorithm with a complete convergence proof. As special cases, we can easily derive performance guarantees for many known regularization types, including ?., ?., . and ?. + . style regularization. Fur