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high-dimensional vector classification

Intelligent Optimization Methods for High Dimensional Data Classification for Support Vector Machines

Intelligent Optimization Methods for High Dimensional Data Classification for Support Vector Machines

... support vector classifiers, since the classification performance is significant when the knowledge concerning the data set is ...SVM classification model that can be used to determine optimized SVM ...

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Neyman-Pearson Classification under High-Dimensional Settings

Neyman-Pearson Classification under High-Dimensional Settings

... One existing solution to asymmetric error control is cost-sensitive learning, which assigns two different costs as weights of the type I/II errors (Elkan, 2001; Lin et al., 2002; Zadrozny et al., 2003). Despite many ...

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Bayesian kernel projections for classification of high dimensional data

Bayesian kernel projections for classification of high dimensional data

... Kernel methods were first introduced into statisti- cal learning by [Aizerman et al., 1964] and later re- introduced by [Boser et al., 1992] who constructed the Support Vector Machine, a generalization of the ...

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Dimension Reduction and Classification for High Dimensional Complex Data.

Dimension Reduction and Classification for High Dimensional Complex Data.

... the High Dimension, Low Sample Size (HDLSS) data and the presence of the matrix-valued predictors pose signicant challenges to the application of ...a vector and performing LDA with vectorized predictors ...

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Flexible High-Dimensional Classification Machines and Their Asymptotic Properties

Flexible High-Dimensional Classification Machines and Their Asymptotic Properties

... large-margin classification methods, Support Vector Machine (SVM) and Distance Weighted Discrimination (DWD), under two contexts: the high-dimensional, low-sample size data and the imbalanced ...

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Unsupervised Dimensionality Reduction for High-Dimensional Data Classification

Unsupervised Dimensionality Reduction for High-Dimensional Data Classification

... reduction classification, combines the two different unsupervised dimension reduction methods, locally linear embedding (LLE) and principal component analysis (PCA) with the five machine learning ...

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Margin Trees for High-dimensional Classification

Margin Trees for High-dimensional Classification

... support vector classifiers at the node, but do not discuss adaptive construction of the tree ...hierarchical classification methods using nearest centroid classifiers at each ...

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Bayesian Classification of High Dimensional Data with Gaussian Process using Different Kernels

Bayesian Classification of High Dimensional Data with Gaussian Process using Different Kernels

... and high dimensional data subject to some redundancy, this is the gap this study is try to ...one-class classification approach which is based on kernel based algorithm of Bayesian ...class ...

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Deep Learning Based Sentiment Analysis for Recommender System

Deep Learning Based Sentiment Analysis for Recommender System

... the classification of the sentiment based on the provided input text ...for classification of linear models such as Support Vector Machines (SVM) or Logistic Regression which takes inputs that are ...

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Fundus Image Classification Using Two Dimensional Linear Discriminant Analysis and Support Vector Machine

Fundus Image Classification Using Two Dimensional Linear Discriminant Analysis and Support Vector Machine

... Diabetic Retinopathy (DR) is a disease of the eye as a result of complications of Diabetes Mellitus (DM). The Symptom of DR is a decrease in vision sharpness; moreover, it is a blindness. The percentage of patients with ...

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An Efficient Image Classification Using Class Imbalance In High-Dimensional Data

An Efficient Image Classification Using Class Imbalance In High-Dimensional Data

... image classification based on large scale learning is widely used for commercial ...image classification based on Class imbalance method by increasing the competitive ...Imbalance Classification ...

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Classification and Analysis of High Dimensional
          Datasets using Clustering and Decision tree

Classification and Analysis of High Dimensional Datasets using Clustering and Decision tree

... Efficient Classification of Data Using Decision Tree was proposed by Bhaskar ...of Classification. The Learning classification techniques in can be classified into three fundamental types; first is ...

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Booster in High Dimensional Data Classification

Booster in High Dimensional Data Classification

... of high dimensional data is becomingmore common in many practical applicationssuch as data mining, machine learning and microarraygene expression data ...others. Vector Space Model (VSM) is a common ...

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Boosting methods for variable selection in high dimensional sparse models

Boosting methods for variable selection in high dimensional sparse models

... in high dimen- sional linear models based on a forward selection version of the least absolute selection and shrinkage operator (LASSO), adaptive LASSO or elastic net, respectively to be called as forward ...

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FEATURE SELECTION BOOSTER ALGORITHM FOR HIGH DIMENSIONAL DATA CLASSIFICATION

FEATURE SELECTION BOOSTER ALGORITHM FOR HIGH DIMENSIONAL DATA CLASSIFICATION

... Three Classifiers with b = 3 and b= 5. Here ensemble mRMR is well recognized for different real datasets. Boosting technique helps the feature selection algorithm to increase the accuracy of the classification and ...

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Booster in High Dimensional Data Classification

Booster in High Dimensional Data Classification

... In classification tree modeling the data is classified to make predictions about new ...of classification trees and shows two methods of pruning ...of classification tree algorithms with different ...

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New Algorithms for Efficient High-Dimensional Nonparametric Classification

New Algorithms for Efficient High-Dimensional Nonparametric Classification

... Several effective solutions exist for this problem when the dimension D is small, including Voronoi diagrams (Preparata and Shamos, 1985), which work well for two dimensional data. Other meth- ods are designed to ...

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Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models

Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models

... our high-dimensional VAR model simulta- neously analyzes a key set of macroeconomic variables and also accounts for the influence of the largest stocks in the ...

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A Framework To Integrate Feature Selection Algorithm For Classification Of  High Dimensional Data

A Framework To Integrate Feature Selection Algorithm For Classification Of High Dimensional Data

... and high-dimensional social media data challenges traditional data mining tasks such as classification and clustering due to curse of dimensionality and scalability ...handle high-dimensional ...

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On the Performance of Latent Semantic Indexing-based Information Retrieval

On the Performance of Latent Semantic Indexing-based Information Retrieval

... A valuable point made by Keen [ 19 ] is that dif- ferences that are not statistically significant can still be important if they occur repeatedly in many different contexts. From these experi- ments we can conclude that ...

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