The boosting approach to machine learning: An overview
RE Schapire - Nonlinear estimation and classification, 2003 - Springer
Boosting is a general method for improving the accuracy of any given learning algorithm.
Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent …
Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent …
Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction
S Lin, H Zheng, B Han, Y Li, C Han, W Li - Acta Geotechnica, 2022 - Springer
Slope engineering is a complex nonlinear system. It is difficult to respond with a high level of
precision and efficiency requirements for stability assessment using conventional theoretical …
precision and efficiency requirements for stability assessment using conventional theoretical …
[图书][B] Foundations of machine learning
A new edition of a graduate-level machine learning textbook that focuses on the analysis
and theory of algorithms. This book is a general introduction to machine learning that can …
and theory of algorithms. This book is a general introduction to machine learning that can …
[图书][B] The nature of statistical learning theory
V Vapnik - 2013 - books.google.com
The aim of this book is to discuss the fundamental ideas which lie behind the statistical
theory of learning and generalization. It considers learning as a general problem of function …
theory of learning and generalization. It considers learning as a general problem of function …
[PDF][PDF] Experiments with a new boosting algorithm
Y Freund, RE Schapire - icml, 1996 - Citeseer
Abstract In an earlier paper [9], we introduced a new “boosting” algorithm called AdaBoost
which, theoretically, can be used to significantly reduce the error of any learning algorithm …
which, theoretically, can be used to significantly reduce the error of any learning algorithm …
A decision-theoretic generalization of on-line learning and an application to boosting
Y Freund, RE Schapire - Journal of computer and system sciences, 1997 - Elsevier
In the first part of the paper we consider the problem of dynamically apportioning resources
among a set of options in a worst-case on-line framework. The model we study can be …
among a set of options in a worst-case on-line framework. The model we study can be …
[图书][B] Pattern recognition and neural networks
BD Ripley - 2007 - books.google.com
Pattern recognition has long been studied in relation to many different (and mainly
unrelated) applications, such as remote sensing, computer vision, space research, and …
unrelated) applications, such as remote sensing, computer vision, space research, and …
A desicion-theoretic generalization of on-line learning and an application to boosting
Y Freund, RE Schapire - European conference on computational learning …, 1995 - Springer
We consider the problem of dynamically apportioning resources among a set of options in a
worst-case on-line framework. The model we study can be interpreted as a broad, abstract …
worst-case on-line framework. The model we study can be interpreted as a broad, abstract …
[PDF][PDF] A short introduction to boosting
Y Freund, R Schapire, N Abe - Journal-Japanese Society For Artificial …, 1999 - yorku.ca
Boosting is a general method for improving the accuracy of any given learning algorithm.
This short overview paper introduces the boosting algorithm AdaBoost, and explains the …
This short overview paper introduces the boosting algorithm AdaBoost, and explains the …
Neural network-based face detection
We present a neural network-based upright frontal face detection system. A retinally
connected neural network examines small windows of an image and decides whether each …
connected neural network examines small windows of an image and decides whether each …