Results 1  10
of
37
No Free Lunch Theorems for Optimization
, 1997
"... A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of “no free lunch ” (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performan ..."
Abstract

Cited by 640 (9 self)
 Add to MetaCart
A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of “no free lunch ” (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to informationtheoretic aspects of optimization and benchmark measures of performance are also presented. Other issues addressed include timevarying optimization problems and a priori “headtohead” minimax distinctions between optimization algorithms, distinctions that result despite the NFL theorems’ enforcing of a type of uniformity over all algorithms.
A generalized Gaussian image model for edgepreserving MAP estimation
 IEEE Trans. on Image Processing
, 1993
"... Absfrucf We present a Markov random field model which allows realistic edge modeling while providing stable maximum a posteriori MAP solutions. The proposed model, which we refer to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distri ..."
Abstract

Cited by 238 (34 self)
 Add to MetaCart
Absfrucf We present a Markov random field model which allows realistic edge modeling while providing stable maximum a posteriori MAP solutions. The proposed model, which we refer to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisifies several desirable analytical and computational properties for MAP estimation, including continuous dependence of the estimate on the data, invariance of the character of solutions to scaling of data, and a solution which lies at the unique global minimum of the U posteriori loglikeihood function. The GGMRF is demonstrated to be useful for image reconstruction in lowdosage transmission tomography. I.
Approximation Algorithms for Classification Problems with Pairwise Relationships: Metric Labeling and Markov Random Fields
 IN IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE
, 1999
"... In a traditional classification problem, we wish to assign one of k labels (or classes) to each of n objects, in a way that is consistent with some observed data that we have about the problem. An active line of research in this area is concerned with classification when one has information about pa ..."
Abstract

Cited by 161 (2 self)
 Add to MetaCart
In a traditional classification problem, we wish to assign one of k labels (or classes) to each of n objects, in a way that is consistent with some observed data that we have about the problem. An active line of research in this area is concerned with classification when one has information about pairwise relationships among the objects to be classified; this issue is one of the principal motivations for the framework of Markov random fields, and it arises in areas such as image processing, biometry, and document analysis. In its most basic form, this style of analysis seeks a classification that optimizes a combinatorial function consisting of assignment costs  based on the individual choice of label we make for each object  and separation costs  based on the pair of choices we make for two "related" objects. We formulate a general classification problem of this type, the metric labeling problem; we show that it contains as special cases a number of standard classification f...
Clustering with instancelevel constraints
 In Proceedings of the Seventeenth International Conference on Machine Learning
, 2000
"... One goal of research in artificial intelligence is to automate tasks that currently require human expertise; this automation is important because it saves time and brings problems that were previously too large to be solved into the feasible domain. Data analysis, or the ability to identify meaningf ..."
Abstract

Cited by 150 (6 self)
 Add to MetaCart
One goal of research in artificial intelligence is to automate tasks that currently require human expertise; this automation is important because it saves time and brings problems that were previously too large to be solved into the feasible domain. Data analysis, or the ability to identify meaningful patterns and trends in large volumes of data, is an important task that falls into this category. Clustering algorithms are a particularly useful group of data analysis tools. These methods are used, for example, to analyze satellite images of the Earth to identify and categorize different land and foliage types or to analyze telescopic observations to determine what distinct types of astronomical bodies exist and to categorize each observation. However, most existing clustering methods apply general similarity techniques rather than making use of problemspecific information. This dissertation first presents a novel method for converting existing clustering algorithms into constrained clustering algorithms. The resulting methods are able to accept domainspecific information in the form of constraints on the output clusters. At the most general level, each constraint is an instancelevel statement
Adaptive Load Balancing: A Study in MultiAgent Learning
 Journal of Artificial Intelligence Research
, 1995
"... We study the process of multiagent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are it ..."
Abstract

Cited by 80 (0 self)
 Add to MetaCart
We study the process of multiagent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency. 1. Introduction This article investigates multiagent reinforcement learning in the context of a concrete problem of undisputed importance  load balancing. Real life provides us with many exampl...
Online retrainable neural networks: improving the performance of neural networks in image analysis problems
 IEEE Trans. Neural Networks
, 2000
"... Abstract—A novel approach is presented in this paper for improving the performance of neuralnetwork classifiers in image recognition, segmentation, or coding applications, based on a retraining procedure at the user level. The procedure includes: 1) a training algorithm for adapting the network wei ..."
Abstract

Cited by 42 (29 self)
 Add to MetaCart
Abstract—A novel approach is presented in this paper for improving the performance of neuralnetwork classifiers in image recognition, segmentation, or coding applications, based on a retraining procedure at the user level. The procedure includes: 1) a training algorithm for adapting the network weights to the current condition; 2) a maximum a posteriori (MAP) estimation procedure for optimally selecting the most representative data of the current environment as retraining data; and 3) a decision mechanism for determining when network retraining should be activated. The training algorithm takes into consideration both the former and the current network knowledge in order to achieve good generalization. The MAP estimation procedure models the network output as a Markov random field (MRF) and optimally selects the set of training inputs and corresponding desired outputs. Results are presented which illustrate the theoretical developments as well as the performance of the proposed approach in reallife experiments. Index Terms—Image analysis, MPEG4, neuralnetwork retraining, segmentation, weight adaptation.
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
 IEEE TRANS. ON PATTERN ANAL. AND MACHINE INTELLIGENCE
, 2002
"... In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant vari ..."
Abstract

Cited by 33 (3 self)
 Add to MetaCart
In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant variation of properties within each region. The estimation algorithms used are stochastic, but because of the multiresolution representation, are fast computationally, requiring only a few iterations per pixel to converge to accurate results, with error rates of 12 percent across a range of image structures and textures. The addition of a simple boundary process gives accurate results even at low resolutions, and consequently at very low computational cost.
On Partially Controlled MultiAgent Systems
 Journal of Artificial Intelligence Research
, 1996
"... Motivated by the control theoretic distinction between controllable and uncontrollable events, we distinguish between two types of agents within a multiagent system: controllable agents, which are directly controlled by the system's designer, and uncontrollable agents, which are not under the des ..."
Abstract

Cited by 29 (1 self)
 Add to MetaCart
Motivated by the control theoretic distinction between controllable and uncontrollable events, we distinguish between two types of agents within a multiagent system: controllable agents, which are directly controlled by the system's designer, and uncontrollable agents, which are not under the designer's direct control. We refer to such systems as partially controlled multiagent systems, and we investigate how one might influence the behavior of the uncontrolled agents through appropriate design of the controlled agents. In particular, we wish to understand which problems are naturally described in these terms? what methods can be applied to influence the uncontrollable agents? what is their effectiveness ? and whether similar methods work across different domains? Using a gametheoretic framework, this paper studies the design of partially controlled multiagent systems in two contexts: in one context, the uncontrollable agents are expected utility maximizers, while in the other th...
CoLearning and the Evolution of Social Activity
, 1993
"... We introduce the notion of colearning, which refers to a process in which several agents simultaneously try to adapt to one another's behavior so as to produce desirable global system properties. Of particular interest are two specific colearning settings, which relate to the emergence of conventi ..."
Abstract

Cited by 28 (2 self)
 Add to MetaCart
We introduce the notion of colearning, which refers to a process in which several agents simultaneously try to adapt to one another's behavior so as to produce desirable global system properties. Of particular interest are two specific colearning settings, which relate to the emergence of conventions and the evolution of cooperation in societies, respectively. We define a basic colearning rule, called Highest Cumulative Reward (HCR), and show that it gives rise to quite nontrivial system dynamics. In general, we are interested in the eventual convergence of the colearning system to desirable states, as well as in the efficiency with which this convergence is attained. Our results on eventual convergence are analytic; the results on efficiency properties include analytic lower bounds as well as empirical upper bounds derived from rigorous computer simulations. 1 Introduction In multiagent AI systems, such as multiplanner systems, it is crucial that the agents agree on certain r...
An exploration of entity models, collective classification and relation description
 In Proceedings of KDD Workshop on Link Analysis and Group Detection
, 2004
"... Traditional information retrieval typically represents data using a bag of words; data mining typically uses a highly structured database representation. This paper explores the middle ground using a representation which we term entity models, in which questions about structured data may be posed an ..."
Abstract

Cited by 19 (2 self)
 Add to MetaCart
Traditional information retrieval typically represents data using a bag of words; data mining typically uses a highly structured database representation. This paper explores the middle ground using a representation which we term entity models, in which questions about structured data may be posed and answered, but the complexities and taskspecific restrictions of ontologies are avoided. An entity model is a language model or word distribution associated with an entity, such as a person, place or organization. Using these perentity language models, entities may be clustered, links may be detected or described with a short summary, entities may be collectively classified, and question answering may be performed. On a corpus of entities extracted from newswire and the Web, we group entities by profession with 90 % accuracy, improve accuracy further on the task of classifying politicians as liberal or conservative using collective classification and conditional random fields, and answer questions about “who a person is ” with mean reciprocal rank (MRR) of 0.52. 1.