• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 20175
  • 8050
  • 4273
  • 1497
  • 1327
  • 1091
  • 883
  • 428
  • 402
  • 366
  • 280
  • 274
  • 254
  • 252
  • 155
  • Tagged with
  • 47559
  • 11287
  • 7548
  • 6983
  • 5961
  • 5188
  • 3951
  • 3509
  • 3469
  • 3390
  • 3262
  • 3060
  • 3018
  • 2973
  • 2905
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
41

The role of abilities in concept learning /

Shiri, Pushpa January 1976 (has links)
No description available.
42

Structured exploration for reinforcement learning

Jong, Nicholas K. 18 December 2012 (has links)
Reinforcement Learning (RL) offers a promising approach towards achieving the dream of autonomous agents that can behave intelligently in the real world. Instead of requiring humans to determine the correct behaviors or sufficient knowledge in advance, RL algorithms allow an agent to acquire the necessary knowledge through direct experience with its environment. Early algorithms guaranteed convergence to optimal behaviors in limited domains, giving hope that simple, universal mechanisms would allow learning agents to succeed at solving a wide variety of complex problems. In practice, the field of RL has struggled to apply these techniques successfully to the full breadth and depth of real-world domains. This thesis extends the reach of RL techniques by demonstrating the synergies among certain key developments in the literature. The first of these developments is model-based exploration, which facilitates theoretical convergence guarantees in finite problems by explicitly reasoning about an agent's certainty in its understanding of its environment. A second branch of research studies function approximation, which generalizes RL to infinite problems by artificially limiting the degrees of freedom in an agent's representation of its environment. The final major advance that this thesis incorporates is hierarchical decomposition, which seeks to improve the efficiency of learning by endowing an agent's knowledge and behavior with the gross structure of its environment. Each of these ideas has intuitive appeal and sustains substantial independent research efforts, but this thesis defines the first RL agent that combines all their benefits in the general case. In showing how to combine these techniques effectively, this thesis investigates the twin issues of generalization and exploration, which lie at the heart of efficient learning. This thesis thus lays the groundwork for the next generation of RL algorithms, which will allow scientific agents to know when it suffices to estimate a plan from current data and when to accept the potential cost of running an experiment to gather new data. / text
43

Mechanisms and constraints underlying implicit sequence learning

Gureckis, Todd Matthew 28 August 2008 (has links)
Not available / text
44

Supporting critical design dialog

Kehoe, Colleen Mary 12 1900 (has links)
No description available.
45

The influence of instructions on relationships between abilities and performance in a concept identification task

Norton, Ruth Elaine January 1976 (has links)
Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1976. / Bibliography: leaves [69]-71. / Microfiche. / v, 71 leaves, 3 leaves of plates col. ill
46

A study of model-based average reward reinforcement learning

Ok, DoKyeong 09 May 1996 (has links)
Reinforcement Learning (RL) is the study of learning agents that improve their performance from rewards and punishments. Most reinforcement learning methods optimize the discounted total reward received by an agent, while, in many domains, the natural criterion is to optimize the average reward per time step. In this thesis, we introduce a model-based average reward reinforcement learning method called "H-learning" and show that it performs better than other average reward and discounted RL methods in the domain of scheduling a simulated Automatic Guided Vehicle (AGV). We also introduce a version of H-learning which automatically explores the unexplored parts of the state space, while always choosing an apparently best action with respect to the current value function. We show that this "Auto-exploratory H-Learning" performs much better than the original H-learning under many previously studied exploration strategies. To scale H-learning to large state spaces, we extend it to learn action models and reward functions in the form of Bayesian networks, and approximate its value function using local linear regression. We show that both of these extensions are very effective in significantly reducing the space requirement of H-learning, and in making it converge much faster in the AGV scheduling task. Further, Auto-exploratory H-learning synergistically combines with Bayesian network model learning and value function approximation by local linear regression, yielding a highly effective average reward RL algorithm. We believe that the algorithms presented here have the potential to scale to large applications in the context of average reward optimization. / Graduation date:1996
47

Calibrating recurrent sliding window classifiers for sequential supervised learning

Joshi, Saket Subhash 03 October 2003 (has links)
Sequential supervised learning problems involve assigning a class label to each item in a sequence. Examples include part-of-speech tagging and text-to-speech mapping. A very general-purpose strategy for solving such problems is to construct a recurrent sliding window (RSW) classifier, which maps some window of the input sequence plus some number of previously-predicted items into a prediction for the next item in the sequence. This paper describes a general purpose implementation of RSW classifiers and discusses the highly practical issue of how to choose the size of the input window and the number of previous predictions to incorporate. Experiments on two real-world domains show that the optimal choices vary from one learning algorithm to another. They also depend on the evaluation criterion (number of correctly-predicted items versus number of correctly-predicted whole sequences). We conclude that window sizes must be chosen by cross-validation. The results have implications for the choice of window sizes for other models including hidden Markov models and conditional random fields. / Graduation date: 2004
48

Interactions of equivalence and other behavioral relations simple successive discrimination training /

Brackney, Ryan. Vaidya, Manish, January 2009 (has links)
Thesis (M.S.)--University of North Texas, Dec., 2009. / Title from title page display. Includes bibliographical references.
49

Perceived attributes to the development of a positive selfconcept from the experiences of adolescents with learning disabilities /

Bernacchio, Charles P., January 2003 (has links) (PDF)
Thesis (Doctor of Education) in Individualized Ph. D. Program--University of Maine, 2003. / Includes vita. Includes bibliographical references (leaves 216-221).
50

Evaluating the engaged institution the conceptualizations and discourses of engagement /

Steel, Victoria A. Placier, Peggy. January 2009 (has links)
Title from PDF of title page (University of Missouri--Columbia, viewed on March 1, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Peggy Placier. Vita. Includes bibliographical references.

Page generated in 0.1197 seconds