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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
of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

by John Duchi, Elad Hazan, Yoram Singer , 2010
"... Stochastic subgradient methods are widely used, well analyzed, and constitute effective tools for optimization and online learning. Stochastic gradient methods ’ popularity and appeal are largely due to their simplicity, as they largely follow predetermined procedural schemes. However, most common s ..."
Abstract - Cited by 311 (3 self) - Add to MetaCart
Stochastic subgradient methods are widely used, well analyzed, and constitute effective tools for optimization and online learning. Stochastic gradient methods ’ popularity and appeal are largely due to their simplicity, as they largely follow predetermined procedural schemes. However, most common

Online learning for matrix factorization and sparse coding

by Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro , 2010
"... Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set in order to ad ..."
Abstract - Cited by 330 (31 self) - Add to MetaCart
to adapt it to specific data. Variations of this problem include dictionary learning in signal processing, non-negative matrix factorization and sparse principal component analysis. In this paper, we propose to address these tasks with a new online optimization algorithm, based on stochastic approximations

Logistic Regression, AdaBoost and Bregman Distances

by Michael Collins, Robert E. Schapire, Yoram Singer , 2000
"... We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt al ..."
Abstract - Cited by 259 (45 self) - Add to MetaCart
We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt

Boosting with the L_2-Loss: Regression and Classification

by Peter Bühlmann, Bin Yu , 2001
"... This paper investigates a variant of boosting, L 2 Boost, which is constructed from a functional gradient descent algorithm with the L 2 -loss function. Based on an explicit stagewise re tting expression of L 2 Boost, the case of (symmetric) linear weak learners is studied in detail in both regressi ..."
Abstract - Cited by 208 (17 self) - Add to MetaCart
, an optimal rate of convergence result holds for both regression and two-class classification. And this boosted smoothing spline adapts to higher order, unknown smoothness. Moreover, a simple expansion of the 0-1 loss function is derived to reveal the importance of the decision boundary, bias reduction

Multiple instance boosting for object detection

by Paul Viola, John C. Platt, Cha Zhang - In NIPS 18 , 2006
"... A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of boosting that we call MIL-Boost. MI ..."
Abstract - Cited by 179 (10 self) - Add to MetaCart
. MILBoost uses cost functions from the Multiple Instance Learning literature combined with the AnyBoost framework. We adapt the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade. Experiments show that the detection rate is up to 1.6 times better using MILBoost

Cache Decay: Exploiting Generational Behavior to Reduce Cache Leakage Power

by Stefanos Kaxiras, Zhigang Hu, Margaret Martonosi - in Proceedings of the 28th International Symposium on Computer Architecture , 2001
"... Power dissipation is increasingly important in CPUs ranging from those intended for mobile use, all the way up to highperformance processors for high-end servers. While the bulk of the power dissipated is dynamic switching power, leakage power is also beginning to be a concern. Chipmakers expect tha ..."
Abstract - Cited by 280 (26 self) - Add to MetaCart
. Because our decay-based techniques have notions of competitive on-line algorithms at their roots, their energy usage can be theoretically bounded at within a factor of two of the optimal oraclebased policy. We also examine adaptive decay-based policies that make energy-minimizing policy choices on a per

Boosted sampling: Approximation algorithms for stochastic optimization problems

by Anupam Gupta, Martin Pál, R. Ravi, Amitabh Sinha - IN: 36TH STOC , 2004
"... Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the STEINER TREE problem, for example, edges must be chosen to connect terminals (clients); in VERTEX COVER, vertices must be chosen t ..."
Abstract - Cited by 98 (23 self) - Add to MetaCart
factor of σ> 1. The goal is to minimize the first stage cost plus the expected second stage cost. We give a general yet simple technique to adapt approximation algorithms for several deterministic problems to their stochastic versions via the following method. • First stage: Draw σ independent sets

Online coordinate boosting

by Raphael Pelossof, Ilia Vovsha, Michael Jones, Cynthia Rudin , 2008
"... We present a new online boosting algorithm for adapting the weights of a boosted classifier, which yields a closer approximation to Freund and Schapire’s AdaBoost algorithm than previous online boosting algorithms. We also contribute a new way of deriving the online algorithm that ties together prev ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
We present a new online boosting algorithm for adapting the weights of a boosted classifier, which yields a closer approximation to Freund and Schapire’s AdaBoost algorithm than previous online boosting algorithms. We also contribute a new way of deriving the online algorithm that ties together

Ripple Joins for Online Aggregation

by Peter J. Haas, Joseph M. Hellerstein
"... We present a new family of join algorithms, called ripple joins, for online processing of multi-table aggregation queries in a relational database management system (dbms). Such queries arise naturally in interactive exploratory decision-support applications. Traditional offline join algorithms are ..."
Abstract - Cited by 188 (11 self) - Add to MetaCart
We present a new family of join algorithms, called ripple joins, for online processing of multi-table aggregation queries in a relational database management system (dbms). Such queries arise naturally in interactive exploratory decision-support applications. Traditional offline join algorithms
Results 1 - 10 of 929