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143
Classification based on hybridization of parametric and nonparametric classifiers
"... Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities lead to linear and quadratic discriminant analysis), and they work well when these model assumptions are valid. Violation in one or more of these parametric ..."
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Cited by 4 (0 self)
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of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest neighbor and kernel based classifiers) are more flexible and free from parametric model assumptions. But statistical instability of these classifiers may lead to poor performance when
In Defense of NearestNeighbor Based Image Classification
"... Stateoftheart image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, nonparametric NearestNeighbor (NN) based image classifiers require no training time and have other favorable properties. However, the large performance gap between th ..."
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Cited by 266 (2 self)
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Stateoftheart image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, nonparametric NearestNeighbor (NN) based image classifiers require no training time and have other favorable properties. However, the large performance gap between
On the use of neighbourhoodbased nonparametric classifiers
, 1997
"... Alternative nonparametric classification schemes, which come from the use of different definitions of neighbourhood, are introduced. In particular, the Nearest Centroid Neighbourhood along with the neighbourhood relation derived from the Gabriel Graph and the Relative Neighbourhood Graph are used t ..."
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Cited by 17 (6 self)
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Alternative nonparametric classification schemes, which come from the use of different definitions of neighbourhood, are introduced. In particular, the Nearest Centroid Neighbourhood along with the neighbourhood relation derived from the Gabriel Graph and the Relative Neighbourhood Graph are used
Binary Classifier Calibration: Nonparametric approach
"... Accurate calibration of probabilistic predictive models learned is critical for many practical prediction and decisionmaking tasks. There are two main categories of methods for building calibrated classifiers. One approach is to develop methods for learning probabilistic models that are wellcalib ..."
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classifier be well calibrated while retaining its discrimination capability. Also, by casting the histogram binning method as a densitybased nonparametric binary classifier, we can extend it using two simple nonparametric density estimation methods. We demonstrate the performance of the proposed
NonParametric Texture Learning
, 1996
"... Texture is one of the most informative visual cues that help us understand our environment. Texture analysis is an important step in many visual tasks, such as scene segmentation, object recognition, and shape and depth perception. In this chapter we consider the problem of texture recognition and p ..."
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Cited by 5 (0 self)
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and provide an overview of our recent work on this topic ([21, 19, 18]). Our method is based on representing textures in frequency and orientation space, and using nonparametric learning schemes for classification. We present stateoftheart recognition results on a 30 texture database and compare
DDClassifier: Nonparametric classification procedures based on DDplots
 J. Amer. Statist. Assoc
, 2012
"... Using the DDplot (depthversusdepth plot), we introduce a new nonparametric classification algorithm and call it a DDclassifier. The algorithm is completely nonparametric, and requires no prior knowledge of the underlying distributions or of the form of the separating curve. Thus it can be appli ..."
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Cited by 13 (1 self)
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Using the DDplot (depthversusdepth plot), we introduce a new nonparametric classification algorithm and call it a DDclassifier. The algorithm is completely nonparametric, and requires no prior knowledge of the underlying distributions or of the form of the separating curve. Thus it can
Nonparametric Nearest Neighbor with Local
"... The kNearest Neighbor algorithm (kNN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our appr ..."
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The kNearest Neighbor algorithm (kNN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our
Binary Classifier Calibration: A Bayesian NonParametric Approach
"... A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time. Well calibrated predictions are particularly important when machine learning models are used in decision analysis. This paper presents two ..."
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two new nonparametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model
Algorithms for nonparametric classifiers in multirelational data mining
, 2006
"... Over the last decades, due to the advances in information technologies, both the industrial and scientific communities have acquired large volumes of data in digital form. Most of these data sets are stored using relational databases consisting of multiple tables and associations. Moreover, the dat ..."
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the relational information provided in these data sets. This thesis proposes two novel solutions to the task of supervised classification in relational domains, based on traditional nonparametric classifiers and built upon relational algebra. The first approach is based on Kernel Density Estimation
Preextracting Method for SVM Classification Based on the Nonparametric
"... With the increase of the training set’s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel preextracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant influen ..."
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With the increase of the training set’s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel preextracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant
Results 1  10
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143