Maximum entropy modeling of species geographic distributions
- Steven J. Phillips, Robert P. Anderson, R. Schapire
- Environmental Science
- 25 January 2006
A decision-theoretic generalization of on-line learning and an application to boosting
- Y. Freund, R. Schapire
- Computer ScienceEuropean Conference on Computational Learning…
- 1 August 1997
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.
Experiments with a New Boosting Algorithm
- Y. Freund, R. Schapire
- Computer ScienceInternational Conference on Machine Learning
- 3 July 1996
This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
Novel methods improve prediction of species' distributions from occurrence data
- J. Elith, C. Graham, N. Zimmermann
- Environmental Science
- 1 April 2006
This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
The Nonstochastic Multiarmed Bandit Problem
- P. Auer, N. Cesa-Bianchi, Y. Freund, R. Schapire
- Computer Science, EconomicsSIAM journal on computing (Print)
- 2002
A solution to the bandit problem in which an adversary, rather than a well-behaved stochastic process, has complete control over the payoffs.
Improved Boosting Algorithms Using Confidence-rated Predictions
- R. Schapire, Y. Singer
- Computer ScienceCOLT' 98
- 24 July 1998
We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a…
A contextual-bandit approach to personalized news article recommendation
- Lihong Li, Wei Chu, J. Langford, R. Schapire
- Computer ScienceThe Web Conference
- 27 February 2010
This work model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.
A maximum entropy approach to species distribution modeling
- Steven J. Phillips, Miroslav DudĂk, R. Schapire
- Computer ScienceInternational Conference on Machine Learning
- 4 July 2004
This work proposes the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features, and investigates the interpretability of models constructed using maxent.
An Efficient Boosting Algorithm for Combining Preferences
- Y. Freund, Raj D. Iyer, R. Schapire, Y. Singer
- Computer ScienceJournal of machine learning research
- 24 July 1998
This work describes and analyze an efficient algorithm called RankBoost for combining preferences based on the boosting approach to machine learning, and gives theoretical results describing the algorithm's behavior both on the training data, and on new test data not seen during training.
The Strength of Weak Learnability
- R. Schapire
- Computer ScienceMachine-mediated learning
- 1 July 1990
In this paper, a method is described for converting a weak learning algorithm into one that achieves arbitrarily high accuracy, and it is shown that these two notions of learnability are equivalent.
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