Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
39,416
Structure Vibrations: An On-Line Interactive Learning Environment
, 2002
"... The aim of this paper is to present our experience focused on the design of didactic units to teach the oscillatory behavior of simple structures. With the utilization of Java technology we develop and build an online interactive learning environment comprised on simulations of oscillators and simpl ..."
Abstract
- Add to MetaCart
The aim of this paper is to present our experience focused on the design of didactic units to teach the oscillatory behavior of simple structures. With the utilization of Java technology we develop and build an online interactive learning environment comprised on simulations of oscillators
A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting
, 1996
"... ..."
On-line and Off-line Handwriting Recognition: A Comprehensive Survey
- IEEE Transactions on Pattern Analysis and Machine Intelligence
"... AbstractÐHandwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten no ..."
Abstract
-
Cited by 495 (8 self)
- Add to MetaCart
notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the on-line
A new learning algorithm for blind signal separation
-
, 1996
"... A new on-line learning algorithm which minimizes a statistical de-pendency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual in-formation (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of ..."
Abstract
-
Cited by 622 (80 self)
- Add to MetaCart
A new on-line learning algorithm which minimizes a statistical de-pendency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual in-formation (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number
Reinforcement Learning I: Introduction
, 1998
"... In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection, search ..."
Abstract
-
Cited by 5614 (118 self)
- Add to MetaCart
, search) plus learning (association, memory). We argue that RL is the only field that seriously addresses the special features of the problem of learning from interaction to achieve long-term goals.
Reinforcement learning: a survey
- Journal of Artificial Intelligence Research
, 1996
"... This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem ..."
Abstract
-
Cited by 1714 (25 self)
- Add to MetaCart
is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues
On-Line Q-Learning Using Connectionist Systems
, 1994
"... Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete finite-state Markovian problems, which is too restrictive for many real-world environments. Therefore, it is desirable to extend these ..."
Abstract
-
Cited by 381 (1 self)
- Add to MetaCart
of different algorithms based around Q-Learning (Watkins 1989) combined with the Temporal Difference algorithm (Sutton 1988), including a new algorithm (Modified Connectionist Q-Learning), and Q() (Peng and Williams 1994). In addition, we present algorithms for applying these updates on-line during trials
Learning and development in neural networks: The importance of starting small
- Cognition
, 1993
"... It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time- childhood- when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain (language ..."
Abstract
-
Cited by 531 (17 self)
- Add to MetaCart
It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time- childhood- when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain
Hierarchical mixtures of experts and the EM algorithm
, 1993
"... We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM’s). Learning is treated as a max-imum likelihood ..."
Abstract
-
Cited by 885 (21 self)
- Add to MetaCart
problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parame-ters of the architecture. We also develop an on-line learning algorithm in which the pa-rameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.
Markov games as a framework for multi-agent reinforcement learning
- IN PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING
, 1994
"... In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior ..."
Abstract
-
Cited by 601 (13 self)
- Add to MetaCart
In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed
Results 1 - 10
of
39,416
