NIPS
2008
13 years 6 months ago
2008
Extensive labeled data for image annotation systems, which learn to assign class labels to image regions, is difficult to obtain. We explore a hybrid model framework for utilizing...
NIPS
2008
13 years 6 months ago
2008
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...
NIPS
2008
13 years 6 months ago
2008
Embeddings of random variables in reproducing kernel Hilbert spaces (RKHSs) may be used to conduct statistical inference based on higher order moments. For sufficiently rich (char...
NIPS
2008
13 years 6 months ago
2008
Covariance estimation for high dimensional vectors is a classically difficult problem in statistical analysis and machine learning. In this paper, we propose a maximum likelihood ...
NIPS
2008
13 years 6 months ago
2008
We study convergence properties of empirical minimization of a stochastic strongly convex objective, where the stochastic component is linear. We show that the value attained by t...
NIPS
2008
13 years 6 months ago
2008
Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data. Approaches such as exponential ...
NIPS
2008
13 years 6 months ago
2008
In this paper we introduce the MeanNN approach for estimation of main information theoretic measures such as differential entropy, mutual information and divergence. As opposed to...
NIPS
2008
13 years 6 months ago
2008
We present an algorithm for solving a broad class of online resource allocation . Our online algorithm can be applied in environments where abstract jobs arrive one at a time, and...
NIPS
2008
13 years 6 months ago
2008
This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian...
NIPS
2008
13 years 6 months ago
2008
Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performan...