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An Application of Recurrent Nets to Phone Probability Estimation
 IEEE Transactions on Neural Networks
, 1994
"... This paper presents an application of recurrent networks for phone probability estimation in large vocabulary speech recognition. The need for efficient exploitation of context information is discussed ..."
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Cited by 222 (8 self)
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This paper presents an application of recurrent networks for phone probability estimation in large vocabulary speech recognition. The need for efficient exploitation of context information is discussed
Probability Estimates for Multiclass Classification by Pairwise Coupling
 Journal of Machine Learning Research
, 2003
"... Pairwise coupling is a popular multiclass classification method that combines together all pairwise comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. ..."
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Cited by 295 (1 self)
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Pairwise coupling is a popular multiclass classification method that combines together all pairwise comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement.
Probability Estimation
, 2006
"... L’accès à ce site Web et l’utilisation de son contenu sont assujettis aux conditions présentées dans le site ..."
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L’accès à ce site Web et l’utilisation de son contenu sont assujettis aux conditions présentées dans le site
Probability estimation
, 2014
"... Active rule scheduling Learning automata a b s t r a c t Active database systems (ADSs) react automatically to the occurrence of predefined events by defining a set of active rules. One of the main modules of an ADS is the rule scheduler, which has a significant impact on the effectiveness and effic ..."
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Active rule scheduling Learning automata a b s t r a c t Active database systems (ADSs) react automatically to the occurrence of predefined events by defining a set of active rules. One of the main modules of an ADS is the rule scheduler, which has a significant impact on the effectiveness and efficiency of ADSs. During the rule scheduling process, the rule scheduler is responsible for choosing one of the activated or readytobeexecuted rules to evaluate its condition section or execute its action section, respectively. This process continues until there is no rule to be evaluated or executed. In this research, we evaluate and compare existing rule scheduling approaches in a laboratory environment based on a threetier architecture. There are criteria used for the evaluation and comparison of rule scheduling approaches: Average Response Time, Throughput, Response Time Standard Deviation, Time Overhead per Transaction, and CPU Utilization. The three first criteria are used to evaluate the effectiveness, and the latter two criteria are used to evaluate the efficiency of rule scheduling approaches. In this paper, a new approach, referred to as EXSJFEsTLA, is proposed to improve the rule scheduling process, using a learning automaton. In our laboratory environment, EXSJFEsTLA is compared with those rule scheduling approaches that are unconstrained as EXSJFEsTLA is. Unconstrained scheduling approaches serially schedule the rules that do not have any priorities or deadlines. The results of experiments revealed that the proposed approach improved the rule scheduling process according to the evaluation criteria. & 2014 Elsevier Ltd. All rights reserved. 1.
Probability estimation for PPM
 In Proceedings NZCSRSC'95. Available from http://www.cs.waikato.ac.nz/wjt
, 1995
"... The state of the art in lossless text compression is the PPM data compression scheme. Two approaches to the problem of selecting the context models used in the scheme are described. One uses an a priori upper bound on the lengths of the contexts, while the other method is unbounded. Several techniqu ..."
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Cited by 22 (1 self)
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techniques that improve the probability estimation are described, including four new methods: partial update exclusions for the unbounded approach, deterministic scaling, recency scaling and multiple probability estimators. Each of these methods improves the performance for both the bounded and unbounded
Quantification via Probability Estimators
"... Abstract—Quantification is the name given to a novel machine learning task which deals with correctly estimating the number of elements of one class in a set of examples. The output of a quantifier is a real value; since training instances are the same as a classification problem, a natural approach ..."
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Cited by 10 (3 self)
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between training and test. Hence, adjusted versions of classify & count have been developed by using modified thresholds. However, previous works have explicitly discarded (without a deep analysis) any possible approach based on the probability estimations of the classifier. In this paper, we present
On classification, ranking, and probability estimation
"... Abstract. Given a binary classification task, a ranker is an algorithm that can sort a set of instances from highest to lowest expectation that the instance is positive. In contrast to a classifier, a ranker does not output class predictions – although it can be turned into a classifier with help of ..."
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Cited by 1 (0 self)
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and differences between classification, ranking, and probability estimation, which leads to a novel connection between the Brier score and ROC curves. Combining LexRank with isotonic regression, which derives probability estimates from the ROC convex hull, results in the lexicographic probability estimator LexProb.
Transforming Classifier Scores into Accurate Multiclass Probability Estimates
, 2002
"... Class membership probability estimates are important for many applications of data mining in which classification outputs are combined with other sources of information for decisionmaking, such as exampledependent misclassification costs, the outputs of other classifiers, or domain knowledge. Prev ..."
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Cited by 117 (5 self)
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Class membership probability estimates are important for many applications of data mining in which classification outputs are combined with other sources of information for decisionmaking, such as exampledependent misclassification costs, the outputs of other classifiers, or domain knowledge
Visualizing Class Probability Estimators
 In Lecture Notes in Artificial Intelligence 2838
, 2003
"... Inducing classi ers that make accurate predictions on future data is a driving force for research in inductive learning. However, also of importance to the users is how to gain information from the models produced. Unfortunately, some of the most powerful inductive learning algorithms generate ..."
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Cited by 4 (0 self)
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of its class probability estimates. It requires the classi er to generate class probabilities but most practical algorithms are able to do so (or can be modi ed to this end).
Hierarchical Probability Estimation
"... Estimating probabilities based on measured numbers of occurrences of events provides a central link from probability theory to real world applications. In an important class of applications the probabilistic events correspond to the digitized outcome of an analog sensor. This paper shows theoretical ..."
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Estimating probabilities based on measured numbers of occurrences of events provides a central link from probability theory to real world applications. In an important class of applications the probabilistic events correspond to the digitized outcome of an analog sensor. This paper shows
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