A Probabilistic Theory of Pattern Recognition

Devroye, Luc; Györfi, László; Lugosi, Gábor

doi:10.1007/978-1-4612-0711-5

Cited by 2,408 publications

(2,376 citation statements)

References 5 publications

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“…A confusion matrix (CM) gives the complete picture of correct and incorrect classification made by classifiers ψ for separate classes (Devroye et al, 1996). The rows (columns) correspond to the true classes (results of classification made by classifier ψ l ), as shown in Table 1.…”

Section: 4mentioning

confidence: 99%

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Trajdos

Kurzyński

2016

International Journal of Applied Mathematics and Computer Science

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Nowadays, multiclassifier systems (MCSs) are being widely applied in various machine learning problems and in many different domains. Over the last two decades, a variety of ensemble systems have been developed, but there is still room for improvement. This paper focuses on developing competence and interclass cross-competence measures which can be applied as a method for classifiers combination. The cross-competence measure allows an ensemble to harness pieces of information obtained from incompetent classifiers instead of removing them from the ensemble. The cross-competence measure originally determined on the basis of a validation set (static mode) can be further easily updated using additional feedback information on correct/incorrect classification during the recognition process (dynamic mode). The analysis of computational and storage complexity of the proposed method is presented. The performance of the MCS with the proposed cross-competence function was experimentally compared against five reference MCSs and one reference MCS for static and dynamic modes, respectively. Results for the static mode show that the proposed technique is comparable with the reference methods in terms of classification accuracy. For the dynamic mode, the system developed achieves the highest classification accuracy, demonstrating the potential of the MCS for practical applications when feedback information is available.

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Section: 4mentioning

confidence: 99%

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Trajdos

Kurzyński

2016

International Journal of Applied Mathematics and Computer Science

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“…Our next goal is to bound d r for (r, θ r )-admissible prefixes. We showed in the proof of Lemma 2 (see (6) and the explanation after it) that…”

Section: Weak Stability Boundmentioning

confidence: 93%

Stable Transductive Learning

El‐Yaniv

Pechyony

2006

Learning Theory

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Abstract. We develop a new error bound for transductive learning algorithms. The slack term in the new bound is a function of a relaxed notion of transductive stability, which measures the sensitivity of the algorithm to most pairwise exchanges of training and test set points. Our bound is based on a novel concentration inequality for symmetric functions of permutations. We also present a simple sampling technique that can estimate, with high probability, the weak stability of transductive learning algorithms with respect to a given dataset. We demonstrate the usefulness of our estimation technique on a well known transductive learning algorithm.

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“…Extensive work in nonparametrics has extended this result to consider the consistency of Stonetype rules under various sampling processes; see, for example, [6,10] and references therein. These models focus on various dependency structures within the training data and assume that a single processor has access to the entire data stream.…”

Section: The Classical Learning Model and Our Departurementioning

confidence: 96%

Distributed Learning in Wireless Sensor Networks

Swami

Zhao

Hong

et al. 2007

Wireless Sensor Networks

106

178

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The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made about a statistical description of nature. In certain applications, such assumptions are warranted and systems designed from these models show promise. However, in other scenarios, prior knowledge is at best vague and translating such knowledge into a statistical model is undesirable. Applications such as these pave the way for a nonparametric study of distributed detection and estimation. In this paper, we review recent work of the authors in which some elementary models for distributed learning are considered. These models are in the spirit of classical work in nonparametric statistics and are applicable to wireless sensor networks.

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A Probabilistic Theory of Pattern Recognition

Cited by 2,408 publications

References 5 publications

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Stable Transductive Learning

Distributed Learning in Wireless Sensor Networks

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