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CVPR
2012
IEEE
6 years 9 months ago
Pose pooling kernels for sub-category recognition
The ability to normalize pose based on super-category landmarks can significantly improve models of individual categories when training data are limited. Previous methods have co...
Ning Zhang, Ryan Farrell, Trevor Darrell
JMLR
2012
6 years 9 months ago
Metric and Kernel Learning Using a Linear Transformation
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
Prateek Jain, Brian Kulis, Jason V. Davis, Inderji...
JMLR
2012
6 years 9 months ago
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming
Sparse additive models are families of d-variate functions with the additive decomposition f∗ = ∑j∈S f∗ j , where S is an unknown subset of cardinality s d. In this paper,...
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
EMNLP
2011
7 years 7 months ago
Relation Extraction with Relation Topics
This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge l...
Chang Wang, James Fan, Aditya Kalyanpur, David Gon...
ICASSP
2011
IEEE
7 years 11 months ago
Motion vector recovery with Gaussian Process Regression
In this paper, we propose a Gaussian Process Regression (GPR) framework for concealment of corrupted motion vectors in predictive video coding of packet video systems. The problem...
Hadi Asheri, Abdolkhalegh Bayati, Hamid R. Rabiee,...
ICASSP
2011
IEEE
7 years 11 months ago
Speaker recognition using multiple kernel learning based on conditional entropy minimization
We applied a multiple kernel learning (MKL) method based on information-theoretic optimization to speaker recognition. Most of the kernel methods applied to speaker recognition sy...
Tetsuji Ogawa, Hideitsu Hino, Nima Reyhani, Noboru...
PAMI
2011
8 years 2 months ago
Kernel Optimization in Discriminant Analysis
— Kernel mapping is one of the most used approaches to intrinsically derive nonlinear classifiers. The idea is to use a kernel function which maps the original nonlinearly separ...
Di You, Onur C. Hamsici, Aleix M. Martínez
ICIP
2010
IEEE
8 years 5 months ago
Image partitioning with kernel mapping and graph cuts
A novel multiregion graph cut image partitioning method combined with kernel mapping is presented. A kernel function transforms implicitly the image data into data of a higher dim...
Mohamed Ben Salah, Amar Mitiche, Ismail Ben Ayed
ML
2008
ACM
110views Machine Learning» more  ML 2008»
8 years 5 months ago
A theory of learning with similarity functions
Kernel functions have become an extremely popular tool in machine learning, with an attractive theory as well. This theory views a kernel as implicitly mapping data points into a ...
Maria-Florina Balcan, Avrim Blum, Nathan Srebro
PR
2007
139views more  PR 2007»
8 years 6 months ago
Learning the kernel matrix by maximizing a KFD-based class separability criterion
The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. In this p...
Dit-Yan Yeung, Hong Chang, Guang Dai
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