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Hierarchical testing designs for pattern recognition
, 2003
"... We explore the theoretical foundations of a “twenty questions” approach to pattern recognition. The object of the analysis is the computational process itself rather than probability distributions (Bayesian inference) or decision boundaries (statistical learning). Our formulation is motivated by app ..."
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Cited by 48 (8 self)
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We explore the theoretical foundations of a “twenty questions” approach to pattern recognition. The object of the analysis is the computational process itself rather than probability distributions (Bayesian inference) or decision boundaries (statistical learning). Our formulation is motivated by applications to scene interpretation in which there are a great many possible explanations for the data, one (“background”) is statistically dominant, and it is imperative to restrict intensive computation to genuinely ambiguous regions. The focus here is then on pattern filtering: Given a large set Y of possible patterns or explanations, narrow down the true one Y to a small (random) subset ̂Y ⊂ Y of “detected ” patterns to be subjected to further, more intense, processing. To this end, we consider a family of hypothesis tests for Y ∈ A versus the nonspecific alternatives Y ∈ A c. Each test has null type I error and the candidate sets A ⊂ Y are arranged in a hierarchy of nested partitions. These tests are then
Daily Rainfall Forecasting using an Ensemble Technique based on Singular Spectrum Analysis
"... In previous work, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the TakensMane theorem and using the Singular Spectrum Analysis in order to reduce the effects of the possible discontinuity of the signal. In this paper we pre ..."
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In previous work, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the TakensMane theorem and using the Singular Spectrum Analysis in order to reduce the effects of the possible discontinuity of the signal. In this paper we present some new results concerning the application of this approach to the forecasting of the individual rainfall intensities series collected by 135 stations distributed in the Tiber basin.
An Ensemble Technique based on Singular Spectrum Analysis applied to Daily Rainfall Forecasting
, 2003
"... In previous work, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the Embedding Theorem, and using the Singular Spectrum Analysis both in order to reduce the effects of the possible discontinuity of the signal and to implement ..."
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Cited by 1 (0 self)
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In previous work, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the Embedding Theorem, and using the Singular Spectrum Analysis both in order to reduce the effects of the possible discontinuity of the signal and to implement an efficient ensemble method. In this paper we present new results concerning the application of this approach to the forecasting of the individual rainfall intensities series collected by 135 stations distributed in the Tiber basin. The average RMS of the obtained predictions is less than 3 mm of rain.
Expanding the Scope of Concept Learning Using Metafeatures
"... We present a general automated preprocessing technique called metafeatures. Using metafeatures, the scope of traditional propositional attributevalue learning is expanded to domains that do not normally fit in the propositional model. These are domains that contain instances that have some kind of ..."
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We present a general automated preprocessing technique called metafeatures. Using metafeatures, the scope of traditional propositional attributevalue learning is expanded to domains that do not normally fit in the propositional model. These are domains that contain instances that have some kind of recurring substructure, such as strokes in handwriting recognition, or local maxima in time series data. Metafeatures are applied to three domains: sign language recognition, ECG classification and Chinese handwriting recognition.