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DISCRIMINATIVE FEATURES FOR LANGUAGE IDENTIFICATION
"... In this paper we investigate the use of discriminatively trained feature transforms to improve the accuracy of a MAP-SVM language recognition system. We train the feature transforms by alternatively solving an SVM optimization on MAP super-vectors estimated from transformed features, and performing ..."
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In this paper we investigate the use of discriminatively trained feature transforms to improve the accuracy of a MAP-SVM language recognition system. We train the feature transforms by alternatively solving an SVM optimization on MAP super-vectors estimated from transformed features, and performing
Using Discriminant Eigenfeatures for Image Retrieval
, 1996
"... This paper describes the automatic selection of features from an image training set using the theories of multi-dimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these Most Discriminating Features for view-based class retrieval ..."
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Cited by 508 (15 self)
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This paper describes the automatic selection of features from an image training set using the theories of multi-dimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these Most Discriminating Features for view-based class
Discriminating Features for Writer Identification
"... Abstract—This paper investigates highly discriminating fea-tures for writer identification for off-line handwritten text lines and passages. Five categories of features are tested: slant and slant energy, skew, pixel distribution, curvature, and entropy. Three experiments are run utilizing the IAM H ..."
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Abstract—This paper investigates highly discriminating fea-tures for writer identification for off-line handwritten text lines and passages. Five categories of features are tested: slant and slant energy, skew, pixel distribution, curvature, and entropy. Three experiments are run utilizing the IAM
Maximum Likelihood Discriminant Feature Spaces
- in Proc. ICASSP
, 2000
"... Linear discriminant analysis (LDA) is known to be inappropriate for the case of classes with unequal sample covariances. In recent years, there has been an interest in generalizing LDA to heteroscedastic discriminant analysis (HDA) by removing the equal within-class covariance constraint. This paper ..."
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Cited by 97 (19 self)
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Linear discriminant analysis (LDA) is known to be inappropriate for the case of classes with unequal sample covariances. In recent years, there has been an interest in generalizing LDA to heteroscedastic discriminant analysis (HDA) by removing the equal within-class covariance constraint
Fisher Discriminant Analysis With Kernels
, 1999
"... A non-linear classification technique based on Fisher's discriminant is proposed. The main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space. The linear classification in feature space corresponds to a (powerful) non-linear decision f ..."
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Cited by 503 (18 self)
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A non-linear classification technique based on Fisher's discriminant is proposed. The main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space. The linear classification in feature space corresponds to a (powerful) non-linear decision
The pyramid match kernel: Discriminative classification with sets of image features
- IN ICCV
, 2005
"... Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondenc ..."
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Cited by 544 (29 self)
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Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve
Selection of Discriminative Features for Translation Texts
"... Beginning in the first century AD, Buddhist texts underwent a series of translations during a period of nearly 1300 years. The identification of the translator, textual apocrypha, and translation style in Buddhist texts are always important issues. This study proposes an approach to find the most di ..."
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discriminative features that characterize the different Buddhist translation texts or other translation texts. We studied five different kinds of features that can be extracted from translation texts and exploited the F-score and SVM classifier to find the most discriminative features. Not only did we use
Discriminative Features via Generalized Eigenvectors
"... Representing examples in a way that is compati-ble with the underlying classifier can greatly en-hance the performance of a learning system. In this paper we investigate scalable techniques for inducing discriminative features by taking ad-vantage of simple second order structure in the data. We foc ..."
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Cited by 4 (1 self)
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Representing examples in a way that is compati-ble with the underlying classifier can greatly en-hance the performance of a learning system. In this paper we investigate scalable techniques for inducing discriminative features by taking ad-vantage of simple second order structure in the data. We
Discriminative Feature Extraction For Speech Recognition
- Proc. IEEE NN-SP Workshop
, 1993
"... Pattern recognition consists of feature extraction and classification over the extracted features. Usually, these two processes are designed separately, entailing that a resulting recognizer is not necessarily optimal in terms of classification accuracy. To overcome this gap in recognizer design, we ..."
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Cited by 6 (2 self)
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, we introduce in this paper a new design concept, named Discriminative Feature Extraction (DFE). DFE is based on a recent discriminative learning theory, Minimum Classification Error formalization /Generalized Probabilistic Descent method, and provides an innovative way to design the entire process
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