Sciweavers

ICML
2008
IEEE
15 years 16 days ago
Accurate max-margin training for structured output spaces
Tsochantaridis et al. (2005) proposed two formulations for maximum margin training of structured spaces: margin scaling and slack scaling. While margin scaling has been extensivel...
Sunita Sarawagi, Rahul Gupta
ICML
2008
IEEE
15 years 16 days ago
Extracting and composing robust features with denoising autoencoders
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to use...
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pi...
ICML
2008
IEEE
15 years 16 days ago
Optimized cutting plane algorithm for support vector machines
We have developed a new Linear Support Vector Machine (SVM) training algorithm called OCAS. Its computational effort scales linearly with the sample size. In an extensive empirica...
Sören Sonnenburg, Vojtech Franc
ICML
2008
IEEE
15 years 16 days ago
Classification using discriminative restricted Boltzmann machines
Recently, many applications for Restricted Boltzmann Machines (RBMs) have been developed for a large variety of learning problems. However, RBMs are usually used as feature extrac...
Hugo Larochelle, Yoshua Bengio
ICML
2008
IEEE
15 years 16 days ago
An RKHS for multi-view learning and manifold co-regularization
Inspired by co-training, many multi-view semi-supervised kernel methods implement the following idea: find a function in each of multiple Reproducing Kernel Hilbert Spaces (RKHSs)...
Vikas Sindhwani, David S. Rosenberg
ICML
2008
IEEE
15 years 16 days ago
A least squares formulation for canonical correlation analysis
Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multi-dimensional variables. It projects both sets of variables int...
Liang Sun, Shuiwang Ji, Jieping Ye
ICML
2008
IEEE
15 years 16 days ago
Robust matching and recognition using context-dependent kernels
The success of kernel methods including support vector machines (SVMs) strongly depends on the design of appropriate kernels. While initially kernels were designed in order to han...
Hichem Sahbi, Jean-Yves Audibert, Jaonary Rabariso...
ICML
2008
IEEE
15 years 16 days ago
Non-parametric policy gradients: a unified treatment of propositional and relational domains
Policy gradient approaches are a powerful instrument for learning how to interact with the environment. Existing approaches have focused on propositional and continuous domains on...
Kristian Kersting, Kurt Driessens
ICML
2008
IEEE
15 years 16 days ago
A dual coordinate descent method for large-scale linear SVM
In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such larg...
Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sat...
ICML
2008
IEEE
15 years 16 days ago
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
Finale Doshi, Joelle Pineau, Nicholas Roy