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Support-Vector Networks
TLDR
The support-vector network is a new learning machine for two-group classification problems. Expand
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The mnist database of handwritten digits
Disclosed is an improved articulated bar flail having shearing edges for efficiently shredding materials. An improved shredder cylinder is disclosed with a plurality of these flails circumferentiallyExpand
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Algorithms for Learning Kernels Based on Centered Alignment
TLDR
We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. Expand
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AUC Optimization vs. Error Rate Minimization
TLDR
The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. Expand
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Two-Stage Learning Kernel Algorithms
TLDR
This paper examines two-stage techniques for learning kernels based on a notion of alignment. Expand
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Comparison of classifier methods: a case study in handwritten digit recognition
TLDR
This paper compares the performance of several classifier algorithms on a standard database of handwritten digits with respect to training time, recognition time, and memory requirements. Expand
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Sample Selection Bias Correction Theory
TLDR
We analyze the effect of an error in that estimation on the accuracy of the hypothesis returned by the learning algorithm for two estimation techniques: a cluster-based estimation technique and kernel mean matching. Expand
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Learning Bounds for Importance Weighting
TLDR
This paper presents an analysis of importance weighting for learning from finite samples and gives a series of theoretical and algorithmic results. Expand
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Learning Non-Linear Combinations of Kernels
TLDR
This paper studies the general problem of learning kernels based on a polynomial combination of base kernels. Expand
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Generalization Bounds for Learning Kernels
TLDR
This paper presents several novel generalization bounds for the problem of learning kernels based on a combinatorial analysis of the Rademacher complexity of the corresponding hypothesis sets. Expand
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