Results 11  20
of
877
Exponentiated Gradient Versus Gradient Descent for Linear Predictors
 Information and Computation
, 1995
"... this paper, we concentrate on linear predictors . To any vector u 2 R ..."
Abstract

Cited by 327 (12 self)
 Add to MetaCart
this paper, we concentrate on linear predictors . To any vector u 2 R
A Framework for Collaborative, ContentBased and Demographic Filtering
 ARTIFICIAL INTELLIGENCE REVIEW
, 1999
"... We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the rat ..."
Abstract

Cited by 319 (6 self)
 Add to MetaCart
We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants.
Online Convex Programming and Generalized Infinitesimal Gradient Ascent
, 2003
"... Convex programming involves a convex set F R and a convex function c : F ! R. The goal of convex programming is to nd a point in F which minimizes c. In this paper, we introduce online convex programming. In online convex programming, the convex set is known in advance, but in each step of some ..."
Abstract

Cited by 298 (4 self)
 Add to MetaCart
(Show Context)
Convex programming involves a convex set F R and a convex function c : F ! R. The goal of convex programming is to nd a point in F which minimizes c. In this paper, we introduce online convex programming. In online convex programming, the convex set is known in advance, but in each step of some repeated optimization problem, one must select a point in F before seeing the cost function for that step. This can be used to model factory production, farm production, and many other industrial optimization problems where one is unaware of the value of the items produced until they have already been constructed. We introduce an algorithm for this domain, apply it to repeated games, and show that it is really a generalization of in nitesimal gradient ascent, and the results here imply that generalized in nitesimal gradient ascent (GIGA) is universally consistent.
ContextSensitive Learning Methods for Text Categorization
 ACM Transactions on Information Systems
, 1996
"... this article, we will investigate the performance of two recently implemented machinelearning algorithms on a number of large text categorization problems. The two algorithms considered are setvalued RIPPER, a recent rulelearning algorithm [Cohen A earlier version of this article appeared in Proc ..."
Abstract

Cited by 291 (13 self)
 Add to MetaCart
this article, we will investigate the performance of two recently implemented machinelearning algorithms on a number of large text categorization problems. The two algorithms considered are setvalued RIPPER, a recent rulelearning algorithm [Cohen A earlier version of this article appeared in Proceedings of the 19th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR) pp. 307315
Tracking the best expert
, 1998
"... We generalize the recent relative loss bounds for online algorithms where the additional loss of the algorithm on the whole sequence of examples over the loss of the best expert is bounded. The generalization allows the sequence to be partitioned into segments, and the goal is to bound the additi ..."
Abstract

Cited by 248 (22 self)
 Add to MetaCart
We generalize the recent relative loss bounds for online algorithms where the additional loss of the algorithm on the whole sequence of examples over the loss of the best expert is bounded. The generalization allows the sequence to be partitioned into segments, and the goal is to bound the additional loss of the algorithm over the sum of the losses of the best experts for each segment. This is to model situations in which the examples change and different experts are best for certain segments of the sequence of examples. In the single segment case, the additional loss is proportional to log n, where n is the number of experts and the constant of proportionality depends on the loss function. Our algorithms do not produce the best partition; however the loss bound shows that our predictions are close to those of the best partition. When the number of segments is k +1and the sequence is of length ℓ, we can bound the additional loss of our algorithm over the best partition by O(k log n + k log(ℓ/k)). For the case when the loss per trial is bounded by one, we obtain an algorithm whose additional loss over the loss of the best partition is independent of the length of the sequence. The additional loss becomes O(k log n + k log(L/k)), where L is the loss of the best partition with k +1segments. Our algorithms for tracking the predictions of the best expert are simple adaptations of Vovk’s original algorithm for the single best expert case. As in the original algorithms, we keep one weight per expert, and spend O(1) time per weight in each trial.
Gambling in a rigged casino: The adversarial multiarmed bandit problem
, 1995
"... In the multiarmed bandit problem, a gambler must decide which arm of K nonidentical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the tradeoff between exploration (trying ou ..."
Abstract

Cited by 242 (8 self)
 Add to MetaCart
In the multiarmed bandit problem, a gambler must decide which arm of K nonidentical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the tradeoff between exploration (trying out each arm to find the best one) and exploitation (playing the arm believed to give the best payoff). Past solutions for the bandit problem have almost always relied on assumptions about the statistics of the slot machines. In this work, we make no statistical assumptions whatsoever about the nature of the process generating the payoffs of the slot machines. We give a solution to the bandit problem in which an adversary, rather than a wellbehaved stochastic process, has complete control over the payoffs. In a sequence of T plays, we prove that the expected perround payoff of our algorithm approaches that of the best arm at the rate O(T \Gamma1=2 ), and we give an improved rate of conver...
Using Confidence Bounds for ExploitationExploration Tradeoffs
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2002
"... We show how a standard tool from statistics  namely confidence bounds  can be used to elegantly deal with situations which exhibit an exploitationexploration tradeoff. Our technique for designing and analyzing algorithms for such situations is general and can be applied when an algorithm ..."
Abstract

Cited by 182 (4 self)
 Add to MetaCart
We show how a standard tool from statistics  namely confidence bounds  can be used to elegantly deal with situations which exhibit an exploitationexploration tradeoff. Our technique for designing and analyzing algorithms for such situations is general and can be applied when an algorithm has to make exploitationversusexploration decisions based on uncertain information provided by a random process. We apply our
Universal prediction
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... This paper consists of an overview on universal prediction from an informationtheoretic perspective. Special attention is given to the notion of probability assignment under the selfinformation loss function, which is directly related to the theory of universal data compression. Both the probabili ..."
Abstract

Cited by 176 (16 self)
 Add to MetaCart
This paper consists of an overview on universal prediction from an informationtheoretic perspective. Special attention is given to the notion of probability assignment under the selfinformation loss function, which is directly related to the theory of universal data compression. Both the probabilistic setting and the deterministic setting of the universal prediction problem are described with emphasis on the analogy and the differences between results in the two settings.
On the Generalization Ability of Online Learning Algorithms
 IEEE Transactions on Information Theory
, 2001
"... In this paper we show that online algorithms for classification and regression can be naturally used to obtain hypotheses with good datadependent tail bounds on their risk. Our results are proven without requiring complicated concentrationofmeasure arguments and they hold for arbitrary onlin ..."
Abstract

Cited by 176 (7 self)
 Add to MetaCart
(Show Context)
In this paper we show that online algorithms for classification and regression can be naturally used to obtain hypotheses with good datadependent tail bounds on their risk. Our results are proven without requiring complicated concentrationofmeasure arguments and they hold for arbitrary online learning algorithms. Furthermore, when applied to concrete online algorithms, our results yield tail bounds that in many cases are comparable or better than the best known bounds.
A Dynamic Disk SpinDown Technique for Mobile Computing
, 1996
"... We address the problem of deciding when to spin down the disk of a mobile computer in order to extend battery life. Since one of the most critical resources in mobile computing environments is battery life, good energy conservation methods can dramatically increase the utility of mobile systems. We ..."
Abstract

Cited by 169 (8 self)
 Add to MetaCart
(Show Context)
We address the problem of deciding when to spin down the disk of a mobile computer in order to extend battery life. Since one of the most critical resources in mobile computing environments is battery life, good energy conservation methods can dramatically increase the utility of mobile systems. We use a simple and efficient algorithm based on machine learning techniques that has excellent performance in practice. Our experimental results are based on traces collected from HP C2474s disks. Using this data, the algorithm outperforms several algorithms that are theoretically optimal in under various worstcase assumptions, as well as the best fixed timeout strategy. In particular, the algorithm reduces the power consumption of the disk to about half (depending on the disk's properties) of the energy consumed by a one minute fixed timeout. Since the algorithm adapts to usage patterns, it uses as little as 88% of the energy consumed by the best fixed timeout computed in retrospect. 1 In...