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The Nature of Statistical Learning Theory

by Vladimir N. Vapnik , 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
Abstract - Cited by 12976 (32 self) - Add to MetaCart
Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

Directional Statistics and Shape Analysis

by K. V. Mardia , 1995
"... There have been various developments in shape analysis in the last decade. We describe here some relationships of shape analysis with directional statistics. For shape, rotations are to be integrated out or to be optimized over whilst they are the basis for directional statistics. However, various c ..."
Abstract - Cited by 775 (31 self) - Add to MetaCart
There have been various developments in shape analysis in the last decade. We describe here some relationships of shape analysis with directional statistics. For shape, rotations are to be integrated out or to be optimized over whilst they are the basis for directional statistics. However, various

On the statistical analysis of dirty pictures

by Julian Besag - JOURNAL OF THE ROYAL STATISTICAL SOCIETY B , 1986
"... ..."
Abstract - Cited by 1242 (5 self) - Add to MetaCart
Abstract not found

Statistical Analysis of Cointegrated Vectors

by Soren Johansen - Journal of Economic Dynamics and Control , 1988
"... We consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors. We then derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimen ..."
Abstract - Cited by 2576 (12 self) - Add to MetaCart
of dimensions. Further we test linear hypotheses about the cointegration vectors. The asymptotic distribution of these test statistics are found and the first is described by a natural multivariate version of the usual test for unit root in an autoregressive process, and the other is a x2 test. 1.

Statistical Analysis with Missing Data

by Roderick J. Little, Nanhua Zhang , 2002
"... Subsample ignorable likelihood for regression ..."
Abstract - Cited by 2650 (20 self) - Add to MetaCart
Subsample ignorable likelihood for regression

An evaluation of statistical approaches to text categorization

by Yiming Yang - Journal of Information Retrieval , 1999
"... Abstract. This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine th ..."
Abstract - Cited by 664 (23 self) - Add to MetaCart
the impact of configuration variations in five versions of Reuters on the observed performance of classifiers. Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were significantly affected by the inclusion of a large portion of unlabelled documents, mading those

Assessing agreement on classification tasks: the kappa statistic

by Jean Carletta - Computational Linguistics , 1996
"... Currently, computational linguists and cognitive scientists working in the area of discourse and dialogue argue that their subjective judgments are reliable using several different statistics, none of which are easily interpretable or comparable to each other. Meanwhile, researchers in content analy ..."
Abstract - Cited by 829 (9 self) - Add to MetaCart
analysis have already experienced the same difficulties and come up with a solution in the kappa statistic. We discuss what is wrong with reliability measures as they are currently used for discourse and dialogue work in computational linguistics and cognitive science, and argue that we would be better off

Self-Similarity Through High-Variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level

by Walter Willinger, Murad S. Taqqu, Robert Sherman, Daniel V. Wilson - IEEE/ACM TRANSACTIONS ON NETWORKING , 1997
"... A number of recent empirical studies of traffic measurements from a variety of working packet networks have convincingly demonstrated that actual network traffic is self-similar or long-range dependent in nature (i.e., bursty over a wide range of time scales) -- in sharp contrast to commonly made tr ..."
Abstract - Cited by 740 (24 self) - Add to MetaCart
traffic modeling assumptions. In this paper, we provide a plausible physical explanation for the occurrence of self-similarity in LAN traffic. Our explanation is based on new convergence results for processes that exhibit high variability (i.e., infinite variance) and is supported by detailed statistical

The Elements of Statistical Learning -- Data Mining, Inference, and Prediction

by Trevor Hastie, Robert Tibshirani, Jerome Friedman
"... ..."
Abstract - Cited by 1320 (13 self) - Add to MetaCart
Abstract not found

High confidence visual recognition of persons by a test of statistical independence

by John G. Daugman - IEEE Trans. on Pattern Analysis and Machine Intelligence , 1993
"... Abstruct- A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a samp ..."
Abstract - Cited by 596 (8 self) - Add to MetaCart
formally to a conditional false accept probability of one in about lo”’. Index Terms- Image analysis, statistical pattern recognition, biometric identification, statistical decision theory, 2-D Gabor filters, wavelets, texture analysis, morphogenesis. I.
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