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Maximum entropy modeling of species geographic distributions
The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements andExpand
A decision-theoretic generalization of on-line learning and an application to boosting
TLDR
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 the multiplicative weightupdate 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. Expand
Experiments with a New Boosting Algorithm
TLDR
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. Expand
Novel methods improve prediction of species' distributions from occurrence data
TLDR
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. Expand
The Nonstochastic Multiarmed Bandit Problem
TLDR
A solution to the bandit problem in which an adversary, rather than a well-behaved stochastic process, has complete control over the payoffs. Expand
An Efficient Boosting Algorithm for Combining Preferences
TLDR
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. Expand
Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By
The main and important contribution of this paper is in establishing a connection between boosting, a newcomer to the statistics scene, and additive models. One of the main properties of boostingExpand
A maximum entropy approach to species distribution modeling
TLDR
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. Expand
A contextual-bandit approach to personalized news article recommendation
TLDR
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. Expand
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
TLDR
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. Expand
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