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705
Sparse Bayesian Learning and the Relevance Vector Machine
, 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vect ..."
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Cited by 966 (5 self)
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This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance
Local features and kernels for classification of texture and object categories: a comprehensive study
 International Journal of Computer Vision
, 2007
"... Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a largescale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations an ..."
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Cited by 653 (34 self)
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and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the χ 2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 476 (46 self)
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in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LSSVM sparse approximation procedure can be successfully applied.
Support vector machine learning for interdependent and structured output spaces
 In ICML
, 2004
"... Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernelbased methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs suchas multiple depe ..."
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Cited by 450 (20 self)
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Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernelbased methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs suchas multiple
Large scale multiple kernel learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... While classical kernelbased learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We s ..."
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Cited by 340 (20 self)
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with sparse feature maps as appear for string kernels, allowing us to train a string kernel SVM on a 10 million realworld splice data set from computational biology. We integrated multiple kernel learning in our machine learning toolbox SHOGUN for which the source code is publicly available at
GPU for Deep Learning Algorithm
"... There are many successful applications to take advantages of massive parallelization on GPU for deep learning algorithm. In this project, I implemented a basic deep learning algorithm, i.e. Autoencoder. Core parts of this project are based on CUBLAS and CUDA kernels. I will first briefly introduce s ..."
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There are many successful applications to take advantages of massive parallelization on GPU for deep learning algorithm. In this project, I implemented a basic deep learning algorithm, i.e. Autoencoder. Core parts of this project are based on CUBLAS and CUDA kernels. I will first briefly introduce
Multiple Kernels for Object Detection
"... Our objective is to obtain a stateofthe art object category detector by employing a stateoftheart image classifier to search for the object in all possible image subwindows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ 2 kernels, ..."
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Cited by 275 (10 self)
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Our objective is to obtain a stateofthe art object category detector by employing a stateoftheart image classifier to search for the object in all possible image subwindows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ 2 kernels
Selftaught learning: Transfer learning from unlabeled data
 Proceedings of the Twentyfourth International Conference on Machine Learning
, 2007
"... We present a new machine learning framework called “selftaught learning ” for using unlabeled data in supervised classification tasks. We do not assume that the unlabeled data follows the same class labels or generative distribution as the labeled data. Thus, we would like to use a large number of ..."
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Cited by 299 (20 self)
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learning widely applicable to many practical learning problems. We describe an approach to selftaught learning that uses sparse coding to construct higherlevel features using the unlabeled data. These features form a succinct input representation and significantly improve classification performance. When
Sparse Greedy Matrix Approximation for Machine Learning
, 2000
"... In kernel based methods such as Regularization Networks large datasets pose signi cant problems since the number of basis functions required for an optimal solution equals the number of samples. We present a sparse greedy approximation technique to construct a compressed representation of the ..."
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Cited by 222 (10 self)
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of the design matrix. Experimental results are given and connections to KernelPCA, Sparse Kernel Feature Analysis, and Matching Pursuit are pointed out. 1. Introduction Many recent advances in machine learning such as Support Vector Machines [Vapnik, 1995], Regularization Networks [Girosi et al., 1995
LeaveOneOut Support Vector Machines
, 1999
"... We present a new learning algorithm for pattern recognition inspired by a recent upper bound on leaveoneout error [ Jaakkola and Haussler, 1999 ] proved for Support Vector Machines (SVMs) [ Vapnik, 1995; 1998 ] . The new approach directly minimizes the expression given by the bound in an attempt ..."
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Cited by 301 (5 self)
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in an attempt to minimize leaveoneout error. This gives a convex optimization problem which constructs a sparse linear classifier in feature space using the kernel technique. As such the algorithm possesses many of the same properties as SVMs. The main novelty of the algorithm is that apart from the choice
Results 1  10
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705