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Ultraconservative Online Algorithms for Multiclass Problems

by Koby Crammer, Yoram Singer - Journal of Machine Learning Research , 2001
"... In this paper we study online classification algorithms for multiclass problems in the mistake bound model. The hypotheses we use maintain one prototype vector per class. Given an input instance, a multiclass hypothesis computes a similarity-score between each prototype and the input instance and th ..."
Abstract - Cited by 320 (21 self) - Add to MetaCart
In this paper we study online classification algorithms for multiclass problems in the mistake bound model. The hypotheses we use maintain one prototype vector per class. Given an input instance, a multiclass hypothesis computes a similarity-score between each prototype and the input instance

Shooting Stars in the Sky: An Online Algorithm for Skyline Queries

by Donald Kossmann, Frank Ramsak, Steffen Rost - In VLDB , 2002
"... Skyline queries ask for a set of interesting points from a potentially large set of data points. If we are traveling, for instance, a restaurant might be interesting if there is no other restaurant which is nearer, cheaper, and has better food. Skyline queries retrieve all such interesting restauran ..."
Abstract - Cited by 284 (0 self) - Add to MetaCart
restaurants so that the user can choose the most promising one. In this paper, we present a new online algorithm that computes the Skyline. Unlike most existing algorithms that compute the Skyline in a batch, this algorithm returns the first results immediately, produces more and more results continuously

On the Power of Randomization in Online Algorithms

by S. Ben-David , A. Borodin, R. Karp, G. Tardos, A. Wigderson
"... ..."
Abstract - Cited by 157 (6 self) - Add to MetaCart
Abstract not found

An Online Algorithm for Segmenting Time Series

by Eamonn Keogh, Selina Chu, David Hart, Michael Pazzani - In ICDM , 2001
"... In recent years, there has been an explosion of interest in mining time series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This represen ..."
Abstract - Cited by 208 (3 self) - Add to MetaCart
. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times

Online passive-aggressive algorithms

by Koby Crammer, Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer - JMLR , 2006
"... We present a unified view for online classification, regression, and uniclass problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. The end result is new alg ..."
Abstract - Cited by 435 (24 self) - Add to MetaCart
We present a unified view for online classification, regression, and uniclass problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. The end result is new

Online Learning with Kernels

by Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson , 2003
"... Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little u ..."
Abstract - Cited by 2831 (123 self) - Add to MetaCart
and computationally efficient algorithms for a wide range of problems such as classification, regression, and novelty detection. In addition to allowing the exploitation of the kernel trick in an online setting, we examine the value of large margins for classification in the online setting with a drifting target. We

Hierarchical mixtures of experts and the EM algorithm

by Michael I. Jordan, Robert A. Jacobs , 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.

A new learning algorithm for blind signal separation

by S. Amari, A. Cichocki, H. H. Yang - , 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

A Sequential Algorithm for Training Text Classifiers

by David D. Lewis, William A. Gale , 1994
"... The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was ..."
Abstract - Cited by 631 (10 self) - Add to MetaCart
The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

by M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon - IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2002
"... Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view o ..."
Abstract - Cited by 2006 (2 self) - Add to MetaCart
Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view
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