Book Chapter10.1007/3-540-61863-5_42
Boosting First-Order Learning
J. Ross Quinlan
- 23 Oct 1996
- pp 143-155
109
TL;DR: Early experimental results from applying boosting to ffoil, a first-order system that constructs definitions of functional relations, suggest that boosting will also prove beneficial for first- order induction.
read more
Abstract: Several empirical studies have confirmed that boosting classifier-learning systems can lead to substantial improvements in predictive accuracy. This paper reports early experimental results from applying boosting to ffoil, a first-order system that constructs definitions of functional relations. Although the evidence is less convincing than that for propositional-level learning systems, it suggests that boosting will also prove beneficial for first-order induction.
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
The Graph Neural Network Model
TL;DR: A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.
Additive Logistic Regression : A Statistical View of Boosting
TL;DR: This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.
Soft Margins for AdaBoost
TL;DR: It is found that ADABOOST asymptotically achieves a hard margin distribution, i.e. the algorithm concentrates its resources on a few hard-to-learn patterns that are interestingly very similar to Support Vectors.
An introduction to boosting and leveraging
Ron Meir,Gunnar Rätsch +1 more
TL;DR: An introduction to theoretical and practical aspects ofboosting and Ensemble learning is provided, providing a useful reference for researchers in the field of Boosting as well as for those seeking to enter this fascinating area of research.
Using prior probabilities in decision-tree classification of remotely sensed data
D.K. McIver,Mark A. Friedl +1 more
TL;DR: A method for incorporating prior probabilities in remote-sensing-based land cover classification using a supervised decision-tree classification algorithm that allows robust probabilities of class membership to be estimated from nonparametric supervised classification algorithms using a technique known as boosting.
236
References
•Book
C4.5: Programs for Machine Learning
J. Ross Quinlan
- 15 Oct 1992
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
27.2K
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.
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.
Inverse entailment and PROGOL
TL;DR: Mode-Directed Inverse Entailment (MDIE) is introduced as a generalisation and enhancement of previous approaches for inverting deduction and an implementation of MDIE in the Progol system is described.
1.5K
Inductive logic programming
Stephen Muggleton
- 01 Feb 1991
TL;DR: A discussion of the feasibility of extending the RLGG framework to allow for the invention of new predicates and the possible relationship between Algorithmic Complexity theory and Probably-Approximately-Correct Learning is discussed.
849
Related Papers (5)
[...]
Leo Breiman
- 01 Aug 1996
J. Ross Quinlan
- 15 Oct 1992
Yoav Freund,Robert E. Schapire +1 more
- 03 Jul 1996