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51
The Maximum Entropy Approach and Probabilistic IR Models
 ACM TRANSACTIONS ON INFORMATION SYSTEMS
, 1998
"... The Principle of Maximum Entropy is discussed and two classic probabilistic models of information retrieval, the Binary Independence Model of Robertson and Sparck Jones and the Combination Match Model of Croft and Harper are derived using the maximum entropy approach. The assumptions on which the cl ..."
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Cited by 12 (0 self)
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The Principle of Maximum Entropy is discussed and two classic probabilistic models of information retrieval, the Binary Independence Model of Robertson and Sparck Jones and the Combination Match Model of Croft and Harper are derived using the maximum entropy approach. The assumptions on which the classical models are based are not made. In their place, the probability distribution of maximum entropy consistent with a set of constraints is determined. It is argued that this subjectivist approach is more philosophically coherent than the frequentist conceptualization of probability that is often assumed as the basis of probabilistic modeling and that this philosophical stance has important practical consequences with respect to the realization of information retrieval research.
Nonparametric image subtraction using grey level scattergrams
 Image and Vision Computing
, 2002
"... Image subtraction is used in many areas of machine vision to identify small changes between equivalent pairs of images. Often only a small subset of the differences will be of interest. In motion analysis only those differences caused by motion are important, and differences due to other sources onl ..."
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Cited by 11 (7 self)
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Image subtraction is used in many areas of machine vision to identify small changes between equivalent pairs of images. Often only a small subset of the differences will be of interest. In motion analysis only those differences caused by motion are important, and differences due to other sources only serve to complicate interpretation. Simple image subtraction detects all differences regardless of their source, and is therefore problematic to use. Superior techniques, analogous to standard statistical tests, can isolate localised differences due to motion from global differences due, for example, to illumination changes. Four such techniques are described. In particular, we introduce a new nonparametric statistical measure which allows a direct probabilistic interpretation of image differences. We expect this to be applicable to a wide range of image formation processes. Its application to medical images is discussed. 1
A Neural Network for Tornado Diagnosis
 Neural Computing and Applications
"... There exist radarbased algorithms designed to detect circulations in the atmosphere. Not all detected circulations, however, are associated with tornados on the ground. Outlined herein, is the development of a multilayered perceptron designed to classify the two types of circulations  nontornadic ..."
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Cited by 8 (2 self)
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There exist radarbased algorithms designed to detect circulations in the atmosphere. Not all detected circulations, however, are associated with tornados on the ground. Outlined herein, is the development of a multilayered perceptron designed to classify the two types of circulations  nontornadic and tornadic  based on various attributes of the circulations. Special emphasis is placed on the role of local minima in determining the optimal architecture via bootstrapping. 1 Introduction A great deal of effort is required to determine the optimal architecture of a Multilayered Perceptron (MLP) designed to perform a specific task. That issue is important to consider because the nonlinearities inherent in a MLP can allow it to possibly overfit data, leading to poor generalization. The nonlinearity of a MLP is determined primarily by two quantities  the number of hidden nodes, and the magnitude of the weights. This can be seen as follows: If the magnitude of the weights is restricted t...
Forecast encompassing tests and probability forecasts
, 2006
"... We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarithmic scoring rules. We propose test statistics for the null of forecast encompassing, present the limiting distributions of the test statistics, and investigate the impact of estimating the forecastin ..."
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Cited by 7 (1 self)
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We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarithmic scoring rules. We propose test statistics for the null of forecast encompassing, present the limiting distributions of the test statistics, and investigate the impact of estimating the forecasting models’ parameters on these distributions. The smallsample performance is investigated, in terms of small numbers of forecasts and model estimation sample sizes. We show the usefulness of the tests for the evaluation of recession probability forecasts from logit models with different leading indicators as explanatory variables, and for evaluating surveybased probability forecasts.
Enhancing the Predictive Performance of Bayesian Graphical Models
 Communications in Statistics – Theory and Methods
, 1995
"... Both knowledgebased systems and statistical models are typically concerned with making predictions about future observables. Here we focus on assessment of predictive performance and provide two techniques for improving the predictive performance of Bayesian graphical models. First, we present Baye ..."
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Cited by 7 (4 self)
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Both knowledgebased systems and statistical models are typically concerned with making predictions about future observables. Here we focus on assessment of predictive performance and provide two techniques for improving the predictive performance of Bayesian graphical models. First, we present Bayesian model averaging, a technique for accounting for model uncertainty. Second, we describe a technique for eliciting a prior distribution for competing models from domain experts. We explore the predictive performance of both techniques in the context of a urological diagnostic problem. KEYWORDS: Prediction; Bayesian graphical model; Bayesian network; Decomposable model; Model uncertainty; Elicitation. 1 Introduction Both statistical methods and knowledgebased systems are typically concerned with combining information from various sources to make inferences about prospective measurements. Inevitably, to combine information, we must make modeling assumptions. It follows that we should car...
Prediction with Expert Advice for the Brier Game
"... We show that the Brier game of prediction is mixable and find the optimal learning rate and substitution function for it. The resulting prediction algorithm is applied to predict results of football and tennis matches. The theoretical performance guarantee turns out to be rather tight on these data ..."
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We show that the Brier game of prediction is mixable and find the optimal learning rate and substitution function for it. The resulting prediction algorithm is applied to predict results of football and tennis matches. The theoretical performance guarantee turns out to be rather tight on these data sets, especially in the case of the more extensive tennis data. 1.
Defensive forecasting for linear protocols
 in Proceedings of the Sixteenth International Conference on Algorithmic Learning Theory
, 2005
"... We consider a general class of forecasting protocols, called “linear protocols”, and discuss several important special cases, including multiclass forecasting. Forecasting is formalized as a game between three players: Reality; Forecaster, whose goal is to predict Reality’s next move; and Skeptic, ..."
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We consider a general class of forecasting protocols, called “linear protocols”, and discuss several important special cases, including multiclass forecasting. Forecasting is formalized as a game between three players: Reality; Forecaster, whose goal is to predict Reality’s next move; and Skeptic, who tries to make money on any lack of agreement between Forecaster and Reality. Our main mathematical result is that for any continuous strategy for Skeptic in a linear protocol there exists a strategy for Forecaster that does not allow Skeptic’s capital to grow. This result is a metatheorem that allows one to transform any constructive law of probability in a linear protocol into a forecasting strategy whose predictions are guaranteed to satisfy this law. We apply this metatheorem to a weak law of large numbers in inner product spaces to obtain a version of the K29 prediction algorithm for linear protocols and show that this version also satisfies the attractive properties of proper calibration and resolution under a suitable choice of its kernel parameter, with no assumptions about the datagenerating mechanism. 1
Internal consistency of survey respondents’forecasts: Evidence based on the Survey of Professional Forecasters
 The Methodology and Practice of Econometrics. A Festschrift in Honour of David F. Hendry. Chapter 8
, 2009
"... We ask whether the di¤erent types of forecasts made by individual survey respondents are mutually consistent, using the SPF survey data. We compare the point forecasts and central tendencies of probability distributions matched by individual respondent, and compare the forecast probabilities of dec ..."
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Cited by 4 (3 self)
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We ask whether the di¤erent types of forecasts made by individual survey respondents are mutually consistent, using the SPF survey data. We compare the point forecasts and central tendencies of probability distributions matched by individual respondent, and compare the forecast probabilities of declines in output with the probabilities implied by the probability distributions. When the expected associations between these di¤erent types of forecasts do not hold for some idividuals, we consider whether the discrepancies we observe are consistent with rational behaviour by agents with asymmetric loss functions. Journal of Economic Literature classi
cation: C53, E32, E37
PIC Matrices: A Computationally Tractable Class of Probabilistic Query Operators
"... this paper, we present a class of query operators which, for reasons to be explained, we have called pic operators. The pic operators comprise a subclass of the general class of query operators that are expressible within the inference network framework. The subclass has been chosen to satisfy two i ..."
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this paper, we present a class of query operators which, for reasons to be explained, we have called pic operators. The pic operators comprise a subclass of the general class of query operators that are expressible within the inference network framework. The subclass has been chosen to satisfy two important design criteria: 1) the operators belonging to the subclass are computationally tractable, and 2) they are intuitively plausible candidates for the modeling of query operators. A wide variety of query operators can be defined as pic operators. The work reported has been principally motivated by the search for more effective Boolean operators. Other approaches to the modeling of Boolean query operators have been tried. As with the inference network, the goal has been to generalize the classical, strict Propositional Logic interpretation of the query operators. The objective of this generalization is twofold. On the one hand, to allow for graduated inputs to the Boolean operators so that the representation of documents in terms of vectors of Boolean characteristics can be extended to vectors of feature weights. Second, to generate real valued operator output so that dichotomous relevance judgments can be replaced by document ranking in response to user's queries. A particularly notable success in this pursuit was reported by Salton, Fox and Wu [Salton et al. 1983; Salton et al. 1983]. Grounded in the geometric metaphor of the vector space model, they defined a general class of "pnorm" operators which extend the traditional operators in a natural way. The experimental results achieved were quite positive. Recent experimentation has shown that system performance is improved by replacing the inference network Boolean operator calculation used in the inquery retrieval syst...
Prediction with expert evaluators’ advice
 Proceedings of the Twentieth International Conference on Algorithmic Learning Theory
, 2009
"... We introduce a new protocol for prediction with expert advice in which each expert evaluates the learner’s and his own performance using a loss function that may change over time and may be different from the loss functions used by the other experts. The learner’s goal is to perform better or not mu ..."
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Cited by 3 (1 self)
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We introduce a new protocol for prediction with expert advice in which each expert evaluates the learner’s and his own performance using a loss function that may change over time and may be different from the loss functions used by the other experts. The learner’s goal is to perform better or not much worse than each expert, as evaluated by that expert, for all experts simultaneously. If the loss functions used by the experts are all proper scoring rules and all mixable, we show that the defensive forecasting algorithm enjoys the same performance guarantee as that attainable by the Aggregating Algorithm in the standard setting and known to be optimal. This result is also applied to the case of “specialist ” (or “sleeping”) experts. In this case, the defensive forecasting algorithm reduces to a simple modification of the Aggregating Algorithm. 1