NDLI: Structural extension to logistic regression: discriminative parameter learning of belief net classifiers

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Structural extension to logistic regression: discriminative parameter learning of belief net classifiers

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Greedy linear value-approximation for factored Markov decision processes

Piecewise linear value function approximation for factored MDPs

Bayesian networks for speech and image integration

Value iteration working with belief subset

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A logic-based model of intentions for multi-agent subcontracting

Reinforcement learning of coordination in cooperative multi-agent systems

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Multi-agent algorithms for solving graphical games

Distributed breakout revisited

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Partial-revelation VCG mechanism for combinatorial auctions

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Language modeling for soft keyboards

CobotDS: a spoken dialogue system for chat

Exploiting auditory fovea in humanoid-human interaction

Towards CST-enhanced summarization

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Automated discovery of composite SAT variable-selection heuristics

Learning for quantified boolean logic satisfiability

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Dynamic restart policies

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Knowledge formation and dialogue using the KRAKEN toolset

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Analogical inference over a common sense database

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BN-tools: a software toolkit for experimentation in BBNs

Optimizing parameter learning using temporal differences

Student modeling for a web-based learning environment: a data mining approach

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Consistency and set intersection

Incrementally solving functional constraints

Multiple sequence alignment using anytime A*

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Generalized features: their application to classification

Organizations of self-interested agents

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A dialogue system with digression handling - an ontology-based approach

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FlexBot, Groo, Patton and Hamlet: research using computer games as a platform

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Fuzzy neural networks in a palm environment

CAUI demonstration: composing music based on human feelings

Perspectives on artificial intelligence planning

Most informative dimension reduction

Structural extension to logistic regression: discriminative parameter learning of belief net classifiers

Content Provider	ACM Digital Library
Author	Greiner, Russell Zhou, Wei
Abstract	Bayesian belief nets (BNs) are often used for classification tasks -- typically to return the most likely "class label" for each specified instance. Many BN-learners, however, attempt to find the BN that maximizes a different objective function (viz., likelihood, rather than classification accuracy), typically by first learning an appropriate graphical structure, then finding the maximal likelihood parameters for that structure. As these parameters may not maximize the classification accuracy, "discriminative learners" follow the alternative approach of seeking the parameters that maximize conditional likelihood (CL), over the distribution of instances the BN will have to classify. This paper first formally specifies this task, and shows how it relates to logistic regression, which corresponds to finding the optimal CL parameters for a naïvebayes structure. After analyzing its inherent (sample and computational) complexity, we then present a general algorithm for this task, ELR, which applies to arbitrary BN structures and which works effectively even when given the incomplete training data. This paper presents empirical evidence that ELR works better than the standard "generative" approach in a variety of situations, especially in common situation where the BN-structure is incorrect.
Starting Page	167
Ending Page	173
Page Count	7
ISBN	0262511290
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2002-07-28
Access Restriction	Subscribed
Subject Keyword	(bayesian) belief nets Logistic regression Pac-learning Classification Computational/sample complexity
Content Type	Text
Resource Type	Article

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