Results 1  10
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23
Competitive online statistics
 International Statistical Review
, 1999
"... A radically new approach to statistical modelling, which combines mathematical techniques of Bayesian statistics with the philosophy of the theory of competitive online algorithms, has arisen over the last decade in computer science (to a large degree, under the influence of Dawid’s prequential sta ..."
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Cited by 65 (10 self)
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A radically new approach to statistical modelling, which combines mathematical techniques of Bayesian statistics with the philosophy of the theory of competitive online algorithms, has arisen over the last decade in computer science (to a large degree, under the influence of Dawid’s prequential statistics). In this approach, which we call “competitive online statistics”, it is not assumed that data are generated by some stochastic mechanism; the bounds derived for the performance of competitive online statistical procedures are guaranteed to hold (and not just hold with high probability or on the average). This paper reviews some results in this area; the new material in it includes the proofs for the performance of the Aggregating Algorithm in the problem of linear regression with square loss. Keywords: Bayes’s rule, competitive online algorithms, linear regression, prequential statistics, worstcase analysis.
Hedging predictions in machine learning
 Comput. J
, 2007
"... Recent advances in machine learning make it possible to design efficient prediction algorithms for data sets with huge numbers of parameters. This article describes a new technique for ‘hedging ’ the predictions output by many such algorithms, including support vector machines, kernel ridge regressi ..."
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Cited by 12 (3 self)
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Recent advances in machine learning make it possible to design efficient prediction algorithms for data sets with huge numbers of parameters. This article describes a new technique for ‘hedging ’ the predictions output by many such algorithms, including support vector machines, kernel ridge regression, kernel nearest neighbours, and by many other stateoftheart methods. The hedged predictions for the labels of new objects include quantitative measures of their own accuracy and reliability. These measures are provably valid under the assumption of randomness, traditional in machine learning: the objects and their labels are assumed to be generated independently from the same probability distribution. In particular, it becomes possible to control (up to statistical fluctuations) the number of erroneous predictions by selecting a suitable confidence level. Validity being achieved automatically, the remaining goal of hedged prediction is efficiency: taking full account of the new objects ’ features and other available information to produce as accurate predictions as possible. This can be done successfully using the powerful machinery of modern machine learning. 1
Ridge Regression Confidence Machine
 In Proceedings of the Eighteenth International Conference on Machine Learning
, 2001
"... Recently kernel based methods for machine ..."
Inductive Confidence Machines for Regression
 IN TAPIO ELOMAA, HEIKKI MANNILA, AND HANNU TOIVONEN, EDITORS, PROCEEDINGS OF THE THIRTEENTH EUROPEAN CONFERENCE ON MACHINE LEARNING
, 2002
"... The existing methods of predicting with confidence give good accuracy and confidence values, but quite often are computationally inefficient. Some partial ..."
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Cited by 10 (6 self)
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The existing methods of predicting with confidence give good accuracy and confidence values, but quite often are computationally inefficient. Some partial
Openset face recognition using transduction
 PAMI
, 2005
"... Abstract: This paper motivates and describes a novel realization of transductive inference that can address the Open Set face recognition task. Open Set operates under the assumption that not all the test probes have mates in the gallery. It either detects the presence of some biometric signature wi ..."
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Cited by 9 (3 self)
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Abstract: This paper motivates and describes a novel realization of transductive inference that can address the Open Set face recognition task. Open Set operates under the assumption that not all the test probes have mates in the gallery. It either detects the presence of some biometric signature within the gallery and finds its identity or rejects it, i.e., it provides for the “none of the above ” answer. The main contribution of the paper is Open Set TCM – kNN (Transduction Confidence Machine – k Nearest Neighbors), which is suitable for multiclass authentication operational scenarios that have to include a rejection option for classes never enrolled in the gallery. Open Set TCM – kNN, driven by the relation between transduction and Kolmogorov complexity, provides a local estimation of the likelihood ratio needed for detection tasks. We provide extensive experimental data to show the feasibility, robustness, and comparative advantages of Open Set TCM – kNN on Open Set identification and watch list (surveillance) tasks using challenging FERET data. Last, we analyze the error structure driven by the fact that most of the errors in identification are due to a relatively small number of face patterns. Open Set TCM kNN is shown to be suitable for PSEI (pattern specific error inhomogeneities) error analysis in order to identify difficult to recognize faces. PSEI analysis improves biometric performance by removing a small number of those difficult to recognize faces responsible for much of the original error in performance and/or by using data fusion.
Comparing the bayes and typicalness frameworks
 In Proceedings of the 12th European Conference on Machine Learning (ECML2001
, 2001
"... Abstract. When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these confidence values have no theoretical base – even though the algorithms ’ predictive performance may be good. ..."
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Cited by 9 (3 self)
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Abstract. When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these confidence values have no theoretical base – even though the algorithms ’ predictive performance may be good. There also exist many successful learning algorithms which only depend on the iid assumption. Often however they produce no confidence values for their predictions. Bayesian frameworks are often applied to these algorithms in order to obtain such values, however they can rely on unjustified priors. In this paper 1 we outline the typicalness framework which can be used in conjunction with many other machine learning algorithms. The framework provides confidence information based only on the standard iid assumption and so is much more robust to different underlying data distributions. We show how the framework can be applied to existing algorithms. We also present experimental results which show that the typicalness approach performs close to Bayes when the prior is known to be correct. Unlike Bayes however, the method still gives accurate confidence values even when different data distributions are considered. 1
Plant promoter prediction with confidence estimation
 Nucleic Acids Res
, 2005
"... Accurate prediction of promoters is fundamental to understanding gene expression patterns, where confidence estimation is one of the main requirements. Using recently developed transductive confidence machine (TCM) techniques, we developed a new program TSSPTCM for the prediction of plant promoters ..."
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Cited by 7 (2 self)
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Accurate prediction of promoters is fundamental to understanding gene expression patterns, where confidence estimation is one of the main requirements. Using recently developed transductive confidence machine (TCM) techniques, we developed a new program TSSPTCM for the prediction of plant promoters that also provides confidence of the prediction. The program was trained on 132 and 104 sequences and tested on 40 and 25 sequences (containing TATA and TATAless promoters, respectively) with known transcription start sites (TSSs). As negative training samples for TCM learning we used coding and intron sequences of plant genes annotated in the GenBank. In the test set of TATA promoters, the program correctlypredictedTSSfor35outof40(87.5%)geneswith a median deviation of several base pairs from the true site location. For 25 TATAless promoters, TSSs were predicted for 21 out of 25 (84%) genes, including 14 cases of 5 bp distance between annotated and predicted TSSs. Using TSSPTCM program we annotated promoters in the whole Arabidopsis genome. The predicted promoters were in good agreement with the start position of known Arabidopsis mRNAs. Thus, TCM technique has produced a plantoriented promoter prediction tool of high accuracy. TSSPTCM program and annotated promoters are available at
Computationally efficient transductive machines
 ALT'00 Proceedings
, 2000
"... Abstract. In this paper 1 we propose a new algorithm for providing confidence and credibility values for predictions on a multiclass pattern recognition problem which uses Support Vector machines in its implementation. Previous algorithms which have been proposed to achieve this are very processing ..."
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Cited by 6 (5 self)
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Abstract. In this paper 1 we propose a new algorithm for providing confidence and credibility values for predictions on a multiclass pattern recognition problem which uses Support Vector machines in its implementation. Previous algorithms which have been proposed to achieve this are very processing intensive and are only practical for small data sets. We present here a method which overcomes these limitations and can deal with larger data sets (such as the US Postal Service database). The measures of confidence and credibility given by the algorithm are shown empirically to reflect the quality of the predictions obtained by the algorithm, and are comparable to those given by the less computationally efficient method. In addition to this the overall performance of the algorithm is shown to be comparable to other techniques (such as standard Support Vector machines), which simply give flat predictions and do not provide the extra confidence/credibility measures. 1
The typicalness framework: a comparison with the Bayesian approach
 Department of Computer Science, Royal Holloway, University of London
, 2001
"... When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these confidence values have no theoretical base  even though the algorithms' predictive performance may be good. T ..."
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Cited by 6 (2 self)
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When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these confidence values have no theoretical base  even though the algorithms' predictive performance may be good. There also exist many successful learning algorithms which only depend on the iid assumption. Often however they produce no confidence values for their predictions. Bayesian frameworks are often applied to these algorithms in order to obtain such values, however they can rely on unjustified priors. In this paper we outline the typicalness framework which can be used in conjunction with many other machine learning algorithms. The framework provides confidence information based only on the standard iid assumption and so is much more robust to different underlying data distributions. We show how the framework can be applied to existing algorithms. We also present experimental results which show that the typicalness approach performs close to Bayes when the prior is known to be correct. Unlike Bayes however, the method still gives accurate confidence values even when different data distributions are considered. 1
Testing exchangeability online
 Proceedings of the Twentieth International Conference on Machine Learning
, 2003
"... praktiqeskie vyvody teorii vero�tnoste� mogut bytь obosnovany v kaqestve sledstvi� gipotez o predelьno� pri dannyh ograniqeni�h sloжnosti izuqaemyh �vleni� ..."
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Cited by 3 (1 self)
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praktiqeskie vyvody teorii vero�tnoste� mogut bytь obosnovany v kaqestve sledstvi� gipotez o predelьno� pri dannyh ograniqeni�h sloжnosti izuqaemyh �vleni�