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Filtering by: Creator Kearns, Richard Seng Remove constraint Creator: Kearns, Richard Seng Advisor Tadepalli, Prasad Remove constraint Advisor: Tadepalli, Prasad

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Planning in multi-agent partially observable domains using sparse sampling

Creator:

Donneau-Golencer, Thierry D.

Abstract:

A large number of sequential decision-making problems in uncertain environments can be modeled as Markov Decision Processes (MDPs). In such settings, an agent can observe at each time step the state of the environment and then executes an action, causing a stochastic transition to a new state of the environment...

Resource Type:

Masters Thesis
Bayesian methods for knowledge transfer and policy search in reinforcement learning

Creator:

Wilson, Aaron

Abstract:

How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented...

Resource Type:

Dissertation
Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms

Creator:

Liere, Ray

Abstract:

We developed and investigated machine learning methods that require minimal preprocessing of the input data, use few training examples, run fast, and still obtain high levels of accuracy. Most approaches to designing machine learning programs are based on the supervised learning paradigm – training examples are chosen randomly and given...

Resource Type:

Dissertation

Full Text:

Bylander, Ellen Voorhees, Marti Hearst, Efthimis Efthimiadis, Susan Dumais, Michael Kearns, David Evans
Monte-Carlo planning for probabilistic domains

Creator:

Bjarnason, Ronald V.

Abstract:

This thesis presents a progression of novel planning algorithms that culminates in a new family of diverse Monte-Carlo methods for probabilistic planning domains. We provide a proof for performance guarantees and analyze how these algorithms can resolve some of the shortcomings of traditional probabilistic planning methods. The direct policy search...

Resource Type:

Dissertation
Structured gradient boosting

Creator:

Parker, Charles (Charles Lincoln)

Abstract:

The goal of many machine learning problems can be formalized as the creation of a function that can properly classify an input vector, given a set of examples of that function. While this formalism has produced a number of success stories, there are notable situations in which it fails. One...

Resource Type:

Dissertation

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Academic Affiliation

Electrical Engineering and Computer Science4
Computer Science1

Advisor

Tadepalli, Prasad[remove]5
Fern, Alan2

Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2000 to 2013

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Committee Member

Cull, Paul2
Niess, Margaret2
Cook, Curtis1
Eric, Mortensen1
Fern, Alan1
more Committee Members »

Creator

Bjarnason, Ronald V.1
Donneau Golencer, Thierry D.1
Liere, Ray1
Parker, Charles (Charles Lincoln)1
Wilson, Aaron1

Date

Date range begin – Date range end

Current results range from 1999 to 2012

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Decade

2010-20191
2000-20093
1990-19991

Degree Field

Computer Science4
Electrical and Computer Engineering1

Degree Level

Doctoral4
Master's1

Degree Name

Doctor of Philosophy (Ph.D.)4
Master of Science (M.S.)1

File Format

application/pdf2

Language

English [eng]5

License

All rights reserved4

Non-Academic Affiliation

Oregon State University. Graduate School5

Peer Reviewed

No4

Resource Type

Dissertation4
Masters Thesis1

Rights Statement

In Copyright4
Copyright Not Evaluated1

Subject

Machine learning3
Artificial intelligence -- Mathematical models1
Bayesian statistical decision theory1
Computer algorithms1
Decision making -- Computer programs1
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