Results 21  30
of
2,110
Support Vector Machines for Classification and Regression
 UNIVERSITY OF SOUTHAMPTON, TECHNICAL REPORT
, 1998
"... The problem of empirical data modelling is germane to many engineering applications.
In empirical data modelling a process of induction is used to build up a model of the
system, from which it is hoped to deduce responses of the system that have yet to be observed.
Ultimately the quantity and qualit ..."
Abstract

Cited by 195 (5 self)
 Add to MetaCart
The problem of empirical data modelling is germane to many engineering applications.
In empirical data modelling a process of induction is used to build up a model of the
system, from which it is hoped to deduce responses of the system that have yet to be observed.
Ultimately the quantity and quality of the observations govern the performance
of this empirical model. By its observational nature data obtained is finite and sampled;
typically this sampling is nonuniform and due to the high dimensional nature of the
problem the data will form only a sparse distribution in the input space. Consequently
the problem is nearly always ill posed (Poggio et al., 1985) in the sense of Hadamard
(Hadamard, 1923). Traditional neural network approaches have suffered difficulties with
generalisation, producing models that can overfit the data. This is a consequence of the
optimisation algorithms used for parameter selection and the statistical measures used
to select the ’best’ model. The foundations of Support Vector Machines (SVM) have
been developed by Vapnik (1995) and are gaining popularity due to many attractive
features, and promising empirical performance. The formulation embodies the Structural
Risk Minimisation (SRM) principle, which has been shown to be superior, (Gunn
et al., 1997), to traditional Empirical Risk Minimisation (ERM) principle, employed by
conventional neural networks. SRM minimises an upper bound on the expected risk,
as opposed to ERM that minimises the error on the training data. It is this difference
which equips SVM with a greater ability to generalise, which is the goal in statistical
learning. SVMs were developed to solve the classification problem, but recently they
have been extended to the domain of regression problems (Vapnik et al., 1997). In the
literature the terminology for SVMs can be slightly confusing. The term SVM is typically
used to describe classification with support vector methods and support vector
regression is used to describe regression with support vector methods. In this report
the term SVM will refer to both classification and regression methods, and the terms
Support Vector Classification (SVC) and Support Vector Regression (SVR) will be used
for specification. This section continues with a brief introduction to the structural risk
The interactive museum tourguide robot
, 1998
"... This paper describes the software architecture of an autonomous tourguide/tutor robot. This robot was recently deployed in the “Deutsches Museum Bonn, ” were it guided hundreds of visitors through the museum during a sixday deployment period. The robot’s control software integrates lowlevel proba ..."
Abstract

Cited by 188 (32 self)
 Add to MetaCart
This paper describes the software architecture of an autonomous tourguide/tutor robot. This robot was recently deployed in the “Deutsches Museum Bonn, ” were it guided hundreds of visitors through the museum during a sixday deployment period. The robot’s control software integrates lowlevel probabilistic reasoning with highlevel problem solving embedded in first order logic. A collection of software innovations, described in this paper, enabled the robot to navigate at high speeds through dense crowds, while reliably avoiding collisions with obstacles—some of which could not even be perceived. Also described in this paper is a user interface tailored towards nonexpert users, which was essential for the robot’s success in the museum. Based on these experiences, this paper argues that time is ripe for the development of AIbased commercial service robots that assist people in everyday life.
Reinforcement Learning with Replacing Eligibility Traces
 MACHINE LEARNING
, 1996
"... The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we introduce a new kind of eligibility trace, the replacing trace, analyze it theoretically, and show that it results in faster, more reliable learning than the conventional ..."
Abstract

Cited by 187 (11 self)
 Add to MetaCart
The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we introduce a new kind of eligibility trace, the replacing trace, analyze it theoretically, and show that it results in faster, more reliable learning than the conventional trace. Both kinds of trace assign credit to prior events according to how recently they occurred, but only the conventional trace gives greater credit to repeated events. Our analysis is for conventional and replacetrace versions of the offline TD(1) algorithm applied to undiscounted absorbing Markov chains. First, we show that these methods converge under repeated presentations of the training set to the same predictions as two well known Monte Carlo methods. We then analyze the relative efficiency of the two Monte Carlo methods. We show that the method corresponding to conventional TD is biased, whereas the method corresponding to replacetrace TD is unbiased. In addition, we show that t...
An Overview of QualityofService Routing for the Next Generation HighSpeed Networks: Problems and Solutions
"... The upcoming Gbps highspeed networks are expected to support a wide range of communicationintensive, realtime multimedia applications. The requirement for timely delivery of digitized audiovisual information raises new challenges for the next generation integratedservice broadband networks. On ..."
Abstract

Cited by 183 (17 self)
 Add to MetaCart
The upcoming Gbps highspeed networks are expected to support a wide range of communicationintensive, realtime multimedia applications. The requirement for timely delivery of digitized audiovisual information raises new challenges for the next generation integratedservice broadband networks. One of the key issues is the QualityofService (QoS) routing. It selects network routes with sufficient resources for the requested QoS parameters. The goal of routing solutions is twofold: (1) satisfying the QoS requirements for every admitted connection and (2) achieving the global efficiency in resource utilization. Many unicast/multicast QoS routing algorithms were published recently, and they work with a variety of QoS requirements and resource constraints. Overall, they can be partitioned into three broad classes: (1) source routing, (2) distributed routing and (3) hierarchical routing algorithms. In this paper we give an overview of the QoS routing problem as well as the existing solutions. We present the strengths and the weaknesses of different routing strategies and outline the challenges. We also discuss the basic algorithms in each class, classify and compare them, and point out possible future directions in the QoS routing area.
Steps toward artificial intelligence
 Computers and Thought
, 1961
"... Harvard University. The work toward attaining "artificial intelligence’ ’ is the center of considerable computer research, design, and application. The field is in its starting transient, characterized by many varied and independent efforts. Marvin Minsky has been requested to draw this work to ..."
Abstract

Cited by 182 (0 self)
 Add to MetaCart
Harvard University. The work toward attaining "artificial intelligence’ ’ is the center of considerable computer research, design, and application. The field is in its starting transient, characterized by many varied and independent efforts. Marvin Minsky has been requested to draw this work together into a coherent summary, supplement it with appropriate explanatory or theoretical noncomputer information, and introduce his assessment of the state of the art. This paper emphasizes the class of activities in which a generalpurpose computer, complete with a library of basic programs, is further programmed to perform operations leading to ever higherlevel information processing functions such as learning and problem solving. This informative article will be of real interest to both the general Proceedings reader and the computer specialist. The Guest Editor.
Multiagent Learning Using a Variable Learning Rate
 Artificial Intelligence
, 2002
"... Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents and so creates a situation of learning a moving target. Previous learning algorithms hav ..."
Abstract

Cited by 180 (8 self)
 Add to MetaCart
Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents and so creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They either converge to a policy that may not be optimal against the specific opponents' policies, or they may not converge at all. In this article we examine this learning problem in the framework of stochastic games. We look at a number of previous learning algorithms showing how they fail at one of the above criteria. We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings. Specifically, we introduce the WoLF principle, "Win or Learn Fast", for varying the learning rate. We examine this technique theoretically, proving convergence in selfplay on a restricted class of iterated matrix games. We also present empirical results on a variety of more general stochastic games, in situations of selfplay and otherwise, demonstrating the wide applicability of this method.
SPUDD: Stochastic planning using decision diagrams
 In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence
, 1999
"... Recently, structured methods for solving factored Markov decisions processes (MDPs) with large state spaces have been proposed recently to allow dynamic programming to be applied without the need for complete state enumeration. We propose and examine a new value iteration algorithm for MDPs that use ..."
Abstract

Cited by 179 (17 self)
 Add to MetaCart
Recently, structured methods for solving factored Markov decisions processes (MDPs) with large state spaces have been proposed recently to allow dynamic programming to be applied without the need for complete state enumeration. We propose and examine a new value iteration algorithm for MDPs that uses algebraic decision diagrams (ADDs) to represent value functions and policies, assuming an ADD input representation of the MDP. Dynamic programming is implemented via ADD manipulation. We demonstrate our method on a class of large MDPs (up to 63 million states) and show that significant gains can be had when compared to treestructured representations (with up to a thirtyfold reduction in the number of nodes required to represent optimal value functions). 1
Algorithms for Sequential Decision Making
, 1996
"... Sequential decision making is a fundamental task faced by any intelligent agent in an extended interaction with its environment; it is the act of answering the question "What should I do now?" In this thesis, I show how to answer this question when "now" is one of a finite set of states, "do" is one ..."
Abstract

Cited by 177 (8 self)
 Add to MetaCart
Sequential decision making is a fundamental task faced by any intelligent agent in an extended interaction with its environment; it is the act of answering the question "What should I do now?" In this thesis, I show how to answer this question when "now" is one of a finite set of states, "do" is one of a finite set of actions, "should" is maximize a longrun measure of reward, and "I" is an automated planning or learning system (agent). In particular,
Shortlist: a connectionist model of continuous speech recognition
 Cognition
, 1994
"... Previous work has shown how a backpropagation network with recurrent connections can successfully model many aspects of human spoken word recognition (Norris, 1988, 1990, 1992, 1993). However, such networks are unable to revise their decisions in the light of subsequent context. TRACE (McClelland ..."
Abstract

Cited by 173 (7 self)
 Add to MetaCart
Previous work has shown how a backpropagation network with recurrent connections can successfully model many aspects of human spoken word recognition (Norris, 1988, 1990, 1992, 1993). However, such networks are unable to revise their decisions in the light of subsequent context. TRACE (McClelland & Elman, 1986), on the other hand, manages to deal appropriately with following context, but only by using a highly implausible architecture that fails to account for some important experimental results. A new model is presented which displays the more desirable properties of each of these models. In contrast to TRACE the new model is entirely bottomup and can readily perform simulations with vocabularies of tens of thousands of words. 1.
The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length
 Machine Learning
, 1996
"... . We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Suffix Automata (PSA). Though hardness results are known for learning distributions gene ..."
Abstract

Cited by 172 (16 self)
 Add to MetaCart
. We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Suffix Automata (PSA). Though hardness results are known for learning distributions generated by general probabilistic automata, we prove that the algorithm we present can efficiently learn distributions generated by PSAs. In particular, we show that for any target PSA, the KLdivergence between the distribution generated by the target and the distribution generated by the hypothesis the learning algorithm outputs, can be made small with high confidence in polynomial time and sample complexity. The learning algorithm is motivated by applications in humanmachine interaction. Here we present two applications of the algorithm. In the first one we apply the algorithm in order to construct a model of the English language, and use this model to correct corrupted text. In the second ...