Journal Article10.1016/S0031-3203(03)00175-4
Constructing support vector machine ensemble
519
TL;DR: Simulation results for the IRIS data classification and the hand-written digit recognition, and the fraud detection show that the proposed SVM ensemble with bagging or boosting outperforms a single SVM in terms of classification accuracy greatly.
read more
About: This article is published in Pattern Recognition. The article was published on 01 Dec 2003. The article focuses on the topics: Bootstrap aggregating & Ranking SVM.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A survey of image classification methods and techniques for improving classification performance
Dengsheng Lu,Qihao Weng +1 more
TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends
TL;DR: The models, methods, and image analysis algorithms in urban remote sensing have been largely developed for the imagery of medium resolution (10–100 m), and the advent of high spatial resolution satellite images, spaceborne hyperspectral images, and LiDAR data is stimulating new research idea, and is driving the future research trends with new models and algorithms.
1.1K
Machine Learning for Internet of Things Data Analysis: A Survey
Mohammad Saeid Mahdavinejad,Mohammad Saeid Mahdavinejad,Mohammadreza Rezvan,Mohammadreza Rezvan,Mohammadamin Barekatain,Peyman Adibi,Payam Barnaghi,Amit P. Sheth +7 more
TL;DR: This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case and presents a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information.
1K
Theory and application of near infrared reflectance spectroscopy in determination of food quality
Haiyan Cen,Yong He +1 more
TL;DR: In this article, the authors present an overview of the type of information that can be obtained based on some developed theory and food research of near infrared reflectance spectroscopy (NIRS), and some problems which need to be solved or investigated further are also discussed.
943
Ensemble approaches for regression: A survey
TL;DR: Different approaches to each of these phases that are able to deal with the regression problem are discussed, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields.
702
References
•Book
The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
- 01 Aug 1997
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Bagging predictors
Leo Breiman
- 01 Aug 1996
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.