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

缩影

容易生皱纹

On Optimal Learning Algorithms for Multiplicity Automataory to simplify the algorithms and the proofs of their correctness. We improve the arithmetic complexity of the problem and argue that it is almost optimal. Then we prove tight bound for the minimal number of equivalence queries and almost (up to . factor) tight bound for the number of membership queries.

muffler

Teaching Randomized Learners only or teaching with inconsistent teachers..Furthermore, we provide characterization theorems for teachability from positive data for both ordinary teachers and inconsistent teachers with and without feedback.

拖网

好忠告人

休息

沉默

STIT

Exact Learning Composed Classes with a Small Number of Mistakesattribute efficient learnable class with a learnable class with polynomial shatter coefficient gives a learnable class..This result extends many results in the literature and gives polynomial learning algorithms for new classes.

污秽

capillaries

Learning Bounds for Support Vector Machines with Learned Kernels in previous (multiplicative) bounds, there is no non-negativity requirement on the coefficients of the linear combinations. We also give simple bounds on the pseudodimension for families of Gaussian kernels.