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      Fault DetectionMultiple Classifier SystemsData Collection
The aim of this paper is to propose a simple procedure that a priori determines a minimum number of classifiers to combine in order to obtain a prediction accuracy level similar to the one obtained with the combination of larger... more
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    •   9  
      Multiple Classifier SystemsRandom ForestClassifier CombinationClassifier System
High-rise buildings, which have become a significant part of the urban habitat, is particularly notorious for their delayed completion times. Though, there exists a plethora of studies on construction delays, the problem however is... more
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    •   18  
      Machine LearningConstruction ManagementApplications of Machine LearningSupport Vector Machines
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    •   3  
      Linguistic TypologyMultiple Classifier SystemsHmong-Mien languages
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    • Multiple Classifier Systems
Some languages have both gender and classifiers, contrary to what was once believed possible. We use these interesting languages as a unique window onto nominal classification. They provide the impetus for a new typology, based on the... more
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    •   19  
      Languages and LinguisticsTypologyGenderMorphosyntax
Biometric authentication is a process of verifying an identity claim using a person's behavioural and physiological characteristics. Due to the vulnerability of the system to environmental noise and variation caused by the user, fusion of... more
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    •   5  
      Multiple Classifier SystemsMCSSystem performanceBiometric Authentication
More than a decade ago, combining multiple classifiers was proposed as a possible solution to the problems posed by the traditional pattern classification approach which involved selecting the best classifier from a set of candidates... more
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    •   9  
      Statistical AnalysisMultiple Classifier SystemsMagnetic ResonanceClassifier Combination
— Multiple classifier systems focus on the combination of classifiers to obtain better performance than a single robust one. These systems unfold three major phases: pool generation, selection and integration. One of the most promising... more
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    •   9  
      Optimization (Mathematical Programming)Artificial IntelligenceOperations ResearchPattern Recognition
Breast cancer is the disease most common malignancy affects female population and the number of affected people is the second most common leading cause of cancer deaths among all cancer types in the developing countries. Nowadays, there... more
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    •   11  
      Breast CancerMultiple Classifier SystemsImage ClassificationBreast Cancer Early Detecion and Treatment
Research in the area of human-computer interaction (HCI) increasingly addressed the aspect of integrating some type of emotional intelligence in the system. Such systems must be able to recognize, interprete and create emotions. Although,... more
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      Human Computer InteractionEmotional intelligencePattern RecognitionAffective Computing
This paper presents a multi-expert system for dynamic signature verification. The system combines three experts whose complementar behaviour is achieved by using both different features and verification strategies. The first expert uses... more
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      Multiple Classifier SystemsSignature Verificationexpert SystemRegional Analysis
In this paper, we present biometric person recognition experiments in a real-world car environment using speech, face, and driving signals. We have performed experiments on a subset of the in-car corpus collected at the Nagoya University,... more
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      Speaker RecognitionPrincipal Component AnalysisFace RecognitionMultiple Classifier Systems
In this thesis we describe results of research on the determination of expert weights for the adaptive combination of two or more optical character recognition engines. The research was done within the Product and Application Development... more
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      Multiple IntelligencesMultiple Classifier SystemsOCR
The use of quality measures in pattern classification has recently received a lot of attention in the areas where the deterioration of signal quality is one of the primary causes of classification errors. An example of such domain is... more
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      Multiple Classifier SystemsPattern ClassificationMCSBiometric Authentication
The computational genome-wide annotation of gene functions requires the prediction of hierarchically structured functional classes and can be formalized as a multiclass, multilabel, multipath hierarchical classification problem,... more
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      Multiple Classifier SystemsGene FunctionProtein Function PredictionHierarchical Bayes
In this thesis we provide a unifying framework for two decades of work in an area of Machine Learning known as cost-sensitive Boosting algorithms. This area is concerned with the fact that most real-world prediction problems are... more
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    •   28  
      Machine LearningData MiningClassification (Machine Learning)Pattern Recognition
The problem of multi-modal pattern recognition is considered under the assumption that the kernel-based approach is applicable within each particular modality. The Cartesian product of the linear spaces into which the respective kernels... more
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      Pattern RecognitionSupport Vector MachinesMultiple Classifier SystemsSupport vector machine
The classification of hyperspectral imagery, using multiple classifier systems is discussed and an SVM-based ensemble is introduced. The data set is separated into separate feature subsets using the correlation between the different... more
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    •   8  
      Support Vector MachinesHyperspectral remote sensingMultiple Classifier SystemsRandom Forest
Abstract. We have previously described an incremental learning algorithm, Learn++. NC, for learning from new datasets that may include new concept classes without accessing previously seen data. We now propose an extension, Learn++. UDNC,... more
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      Incremental learningMultiple Classifier Systems
Most conventional learning algorithms require both positive
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      Multiple Classifier SystemsHigh Dimensional DataMinimum Spanning Tree
We address one of the main open issues about the use of diversity in multiple classifier systems: the effectiveness of the explicit use of diversity measures for creation of classifier ensembles. So far, diversity measures have been... more
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      Pattern RecognitionMultiple Classifier Systems
Rapid advances in remote sensing sensor technology have made it recently possible to collect new dense 3D data like Light Detection And Ranging (LIDAR). One of the challenging issues about LIDAR data is classification of these data for... more
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      Remote SensingFeature SelectionMultiple Classifier SystemsClassification
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      Multiple Classifier SystemsComparative Study of Indian Languages & Culture
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    •   9  
      Optimization (Mathematical Programming)Artificial IntelligenceOperations ResearchPattern Recognition
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    •   4  
      Multiple Classifier SystemsHigh Dimensional DataMinimum Spanning Treelearning algorithm
The Papuan language Mian allows us to refine the typology of nominal classification. Mian has two candidate classification systems, differing completely in their formal realization but overlapping considerably in their semantics. To... more
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      Languages and LinguisticsMorphosyntaxCognitive LinguisticsLinguistics
The concept of 'diversity' has been one of the main open issues in the field of multiple classifier systems. In this paper we address a facet of diversity related to its effectiveness for ensemble construction, namely, explicitly using... more
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    • Multiple Classifier Systems
An ensemble of classifiers based algorithm, Learn++, was recently introduced that is capable of incrementally learning new information from datasets that consecutively become available, even if the new data introduce additional classes... more
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      Incremental learningMultiple Classifier SystemsPerformance ImprovementMCS
Mammography is a not invasive diagnostic technique widely used for early detection of breast cancer. One of the main indicants of cancer is the presence of microcalcifications, i.e. small calcium accumulations, often grouped into... more
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    •   5  
      Breast CancerMultiple Classifier SystemsSpatial DistributionEarly Detection
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      Pattern RecognitionMultiple Classifier SystemsSignature VerificationSelection Combining
Ensemble methods with Random Oracles have been proposed recently . A random-oracle classifier consists of a pair of classifiers and a fixed, randomly created oracle that selects between them. Ensembles of random-oracle decision trees were... more
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      Multiple Classifier SystemsDecision TreeNaive Bayes Classifier
Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems. However, SVMs suffer from the catastrophic forgetting phenomenon, which results in loss of previously learned... more
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    •   4  
      Support Vector MachinesIncremental learningMultiple Classifier SystemsSupport vector machine
We consider a general scheme of parallel classifier combinations in the framework of statistical pattern recognition. Each statistical classifier defines a set of output variables in terms of a posteriori probabilities, i.e. it is used as... more
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    •   7  
      Multiple Classifier SystemsClassifier CombinationInformation Analysisclassifier Fusion
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    • Multiple Classifier Systems
Mammography is a not invasive diagnostic technique widely used for early detection of breast cancer. One of the main indicants of cancer is the presence of microcalcifications, i.e. small calcium accumulations, often grouped into... more
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    •   5  
      Breast CancerMultiple Classifier SystemsSpatial DistributionEarly Detection
Asymmetric classification problems are characterized by class imbalance or unequal costs for different types of misclassifications. One of the main cited weaknesses of AdaBoost is its perceived inability to handle asymmetric problems. As... more
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    •   19  
      Machine LearningData MiningClassification (Machine Learning)Pattern Recognition
The dynamical evolution of weights in the AdaBoost algorithm contains useful information about the rôle that the associated data points play in the built of the AdaBoost model. In particular, the dynamics induces a bipartition of the data... more
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    •   6  
      Pattern RecognitionEntropyMultiple Classifier SystemsClassification
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a... more
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      Computer ScienceMultiple Classifier Systems
Recent findings in the domain of combining classifiers provide a surprising revision of the usefulness of diversity for modelling combined performance. Although there is a common agreement that a successful fusion system should be... more
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      Data AnalysisPattern RecognitionMultiple Classifier SystemsClassification
The energy yield estimation of a photovoltaic (PV) system operating under partially shaded conditions is a challenging task and a very active area of research. In this paper, we attack this problem with the aid of machine learning... more
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      PhotovoltaicsMachine LearningData MiningRenewable Energy
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      Ensemble MethodsMultiple Classifier Systems
The use of multiple features by a classifier often leads to a reduced probability of error, but the design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability... more
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      Multiple Classifier SystemsSample SizePROBABILITY DENSITYBayesian Classifier
In this paper, we present biometric person recognition experiments in a real-world car environment using speech, face, and driving signals. We have performed experiments on a subset of the in-car corpus collected at the Nagoya University,... more
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    •   9  
      Speaker RecognitionPrincipal Component AnalysisFace RecognitionMultiple Classifier Systems
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    •   2  
      Multiple Classifier SystemsAutomatic Detection
The performance of neural nets can be improved through the use of ensembles of redundant nets. In this paper, some of the available methods of ensemble creation are reviewed and the “test and select” methodolology for ensemble creation is... more
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      Multiple Classifier SystemsNeural Net
Rotation Forest is a recently proposed method for building classifier ensembles using independently trained decision trees. It was found to be more accurate than bagging, AdaBoost and Random Forest ensembles across a collection of... more
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    •   8  
      Pattern RecognitionMultiple Classifier SystemsDiscriminant AnalysisRandom Forest
In this paper we propose a strategy for constructing datadriven kernels, automatically determined by the training examples. Basically, their associated Reproducing Kernel Hilbert Spaces arise from finite sets of linearly independent... more
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      Multiple Classifier SystemsTikhonov RegularizationReproducing Kernel Hilbert Space
In this paper, we present some recent developments of Multiple Classifiers Systems (MCS) for remote sensing applications. Some standard MCS methods (boosting, bagging, consensus theory and random forests) are briefly described and applied... more
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    •   10  
      Remote SensingHyperspectral remote sensingMultiple Classifier SystemsSpatial Information
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    •   9  
      Pattern RecognitionBreast CancerMultiple Classifier Systemsexpert System