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ICML
1999
IEEE
14 years 5 months ago
Implicit Imitation in Multiagent Reinforcement Learning
Imitation is actively being studied as an effective means of learning in multi-agent environments. It allows an agent to learn how to act well (perhaps optimally) by passively obs...
Bob Price, Craig Boutilier
ICML
1999
IEEE
14 years 5 months ago
Learning Policies with External Memory
Leonid Peshkin, Nicolas Meuleau, Leslie Pack Kaelb...
ICML
1999
IEEE
14 years 5 months ago
Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples
E cient learning of DFA is a challenging research problem in grammatical inference. Both exact and approximate (in the PAC sense) identi ability of DFA from examples is known to b...
Rajesh Parekh, Vasant Honavar
ICML
1999
IEEE
14 years 5 months ago
Detecting Motifs from Sequences
The problemofmultipleglobalcomparisonin familiesof biologicalsequences has been wellstudied. Fewer algorithms have been developed for identifying local consensus patterns or motif...
Yuh-Jyh Hu, Suzanne B. Sandmeyer, Dennis F. Kibler
ICML
1999
IEEE
14 years 5 months ago
The Alternating Decision Tree Learning Algorithm
The applicationofboosting procedures to decision tree algorithmshas been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a numbe...
Yoav Freund, Llew Mason
ICML
1999
IEEE
14 years 5 months ago
Making Better Use of Global Discretization
Before applying learning algorithms to datasets, practitioners often globally discretize any numeric attributes. If the algorithm cannot handle numeric attributes directly, prior ...
Eibe Frank, Ian H. Witten
ICML
1999
IEEE
14 years 5 months ago
Abstracting from Robot Sensor Data using Hidden Markov Models
ing from Robot Sensor Data using Hidden Markov Models Laura Firoiu, Paul Cohen Computer Science Department, LGRC University of Massachusetts at Amherst, Box 34610 Amherst, MA 01003...
Laura Firoiu, Paul R. Cohen
ICML
1999
IEEE
14 years 5 months ago
AdaCost: Misclassification Cost-Sensitive Boosting
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boo...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip...
ICML
1999
IEEE
14 years 5 months ago
Hierarchical Models for Screening of Iron Deficiency Anemia
Igor V. Cadez, Christine E. McLaren, Padhraic Smyt...