Bounds are given for the empirical and expected Rademacher complexity of classes of linear transformations from a Hilbert space H to a ...nite dimensional space. The results imply ...
We show how nonlinear embedding algorithms popular for use with shallow semisupervised learning techniques such as kernel methods can be applied to deep multilayer architectures, ...
We propose a general and efficient algorithm for learning low-rank matrices. The proposed algorithm converges super-linearly and can keep the matrix to be learned in a compact fac...
This paper introduces the sparse multilayer perceptron (SMLP) which learns the transformation from the inputs to the targets as in multilayer perceptron (MLP) while the outputs of...
This paper presents an innovative approach to personalize on-line content to the needs of individual learners. We use a regular educational environment, the BlackboardTM Learning ...
Guillermo Power, Hugh C. Davis, Alexandra I. Crist...