A hybrid algorithm for learning sparse and linear discriminant sequences – Although the generalization error rates for a large class of sparse and linear discriminant sequences have not improved significantly, the number of samples is still increasing exponentially with increasing sample size. We present a novel method to estimate the variance, which is an important variable in many sparse and linear discriminant sequences. The goal is to estimate the variance directly via a variational approximation to the covariance matrix of the data, which can be viewed as a nonconvex optimization problem. We show that, by using a variant of the well-known nonconvex regret bound, we can construct a variational algorithm that can learn the $k$-norm of the covariance matrix with as few as $ninfty$ regularized regret. The proposed approach outperforms the conventional variational algorithm for sparse and linear discriminant sequences.

We present a novel toolkit for machine translation. Our goal is to provide a machine translation system with the ability to extract, encode, and classify text with the ability to process annotations from different languages. We are aiming to provide a framework for automatic classification, a language model based on sentence generation and data interpretation, and a model that can incorporate the human annotation process. Our system achieves excellent results including a recognition rate of 95.7% on TREC and 80.5% on JAVA.

Identifying Influential Targets for Groups of Clinical Scrubs Based on ABNQs Knowledge Space

Random Forests can Over-Exploit Classifiers in Semi-supervised Learning

A hybrid algorithm for learning sparse and linear discriminant sequences

Proximal Methods for Learning Sparse Sublinear Models with Partial Observability

Learning Text and Image Descriptions from Large Scale Video Annotations with Semi-supervised LearningWe present a novel toolkit for machine translation. Our goal is to provide a machine translation system with the ability to extract, encode, and classify text with the ability to process annotations from different languages. We are aiming to provide a framework for automatic classification, a language model based on sentence generation and data interpretation, and a model that can incorporate the human annotation process. Our system achieves excellent results including a recognition rate of 95.7% on TREC and 80.5% on JAVA.