Regression and Classification
FilterBoost: Regression and Classification on Large Datasets
... logistic regression technique proposed by Collins, Schapire, & Singer, requires fewer assumptions to achieve bounds equivalent to or better than previous ...both classification and conditional ...
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Gradients Weights improve Regression and Classification
... improves regression rates in a minimax sense, opening up potential directions for further development of feature weighting ...on regression or classification convergence ...unknown regression ...
34
Linear methods for regression and classification with functional data
... Functional data occurs when we observe curves or paths from a stochastic process X t . If for each curve or path we have a single response variable Y, we have a regression problem when Y is numerical, a ...
6
Support Vector Machine and Its Application to Regression and Classification
... in regression modeling and data classification. In the regression problem, the responses are quantitative values which take a continuous range of values, meanwhile, the responses in ...
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A Comparison of Classification/Regression Trees and Logistic Regression in Failure Models
... Furthermore, we also test the accuracy capacity to detect the failure of a non-parametric statistical technique Classification/Regression Trees (CART), in comparison with the classic logistic ...
6
A SUMMARY OF Classification and Regression Tree WITH APPLICATION
... For Regression Tree: Every regression technique contains a single response (output) variable and one or more predictor (input) ...Usual regression tree building procedure allows input variables to be ...
18
Evolving clustering, classification and regression with TEDA
... The algorithms for classification, clustering and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of ...
8
Active learning methods for classification and regression problems
... maximize classification performance, it is necessary to adaptat the first model to the new data ...for classification problems, in Section 6 the active learning approach is used in the regression ...
141
Economic classification and regression problems and neural networks
... model can be also easily modified if the prediction has to be made not one time step ahead, but several time steps ahead. clearly, if the task is to predict y(t) for n consecutive time steps t, t + 1, …, t + n − 1, then ...
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An Ensemble of Optimal Trees for Classification and Regression (OTE)
... on classification and regression are used to assess the performance of the proposed method and compare it with kNN, tree, random forest, node harvest and support vector ...and classification error ...
25
Title: Classification and Regression Tree Method for Forecasting
... Sentiment classification is a special task of text classification whose objective is to classify a text according to the sentimental polarities of opinions it contains ...and Regression Tree) ...
7
Multinomial Logistics Regression for Digital Image Classification
... for classification of remotely sensed ...logistics regression provides the field data reconciliation with the probability from the analyst point of view to sensor’s spectral signature point of view based on ...
7
Regression Tree Classification for Activity Prediction in Smart Homes
... Tree Classification for Activity Prediction in Smart Homes Abstract The growing number of older adults in the population has created an increasing need for health-assistive systems, including prompting ...
10
LCR-Net: Localization-Classification-Regression for Human Pose
... of classification networks can be combined with the smoothness of regression meth- ods by a simple yet elegant modification within the learn- ing ...pose regression models. To this end, we formulate ...
10
6 Classification and Regression Trees, 7 Bagging, and Boosting
... Boosting, like bagging, is a committee-based approach that can be used to improve the accuracy of classification or regression methods. Unlike bagging, which uses a sim- ple averaging of results to obtain ...
27
Regression and Classification with Neural Networks
... If m is large then the direct matrix inversion method gets fiddly but not impossible if you want to be efficient.. • Hard to choose a good learning rate?[r] ...
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Penalized Logistic Regression and Classification of Microarray Data
... Two-class classification Situation: A number of biological samples have been collected, preprocessed and hybridized to microarrays. Each sample can be in one of two classes. Suppose that we have n 1 microarrays ...
54
Extrinsic Gaussian processes for regression and classification on manifolds
... from regression to classifi- cation to spatial ...to regression and classification problems with predictors lying in a large class of manifolds, including spheres, planar shape spaces, a space of positive ...
20
Intelligible Models for Classification and Regression
... Table 2 summarizes the key findings of our study. Entries in the table are the average accuracies on the regression and classification datasets, normalized by the accuracy of Penalized (Iteratively Re- ...
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Quantile regression with group lasso for classification
... of regression models for binary response are very com- mon and models specific to these problems are widely ...quantile regression methods have been proposed for high dimensional prob- ...quantile ...
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