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2,625
Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories
, 2004
"... Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been te ..."
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Cited by 784 (16 self)
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are learnt incrementally in a Bayesian manner. Our incremental algorithm is compared experimentally to an earlier batch Bayesian algorithm, as well as to one based on maximumlikelihood. The incremental and batch versions have comparable classification performance on small training sets, but incremental
Online Learning with Kernels
, 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 socalled kernel trick with the large margin idea. There has been little u ..."
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Cited by 2831 (123 self)
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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 socalled kernel trick with the large margin idea. There has been little
External Memory Algorithms and Data Structures
, 1998
"... Data sets in large applications are often too massive to fit completely inside the computer's internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottleneck. In this paper, we surve ..."
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Cited by 349 (23 self)
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survey the state of the art in the design and analysis of external memory algorithms and data structures (which are sometimes referred to as "EM" or "I/O" or "outofcore" algorithms and data structures). EM algorithms and data structures are often designed and analyzed
Fast Stochastic Alternating Direction Method of Multipliers
"... We propose a new stochastic alternating direction method of multipliers (ADMM) algorithm, which incrementally approximates the full gradient in the linearized ADMM formulation. Besides having a low periteration complexity as existing stochastic ADMM algorithms, it improves the convergence rate ..."
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Cited by 4 (0 self)
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on convex problems fromO(1/√T) toO(1/T), where T is the number of iterations. This matches the convergence rate of the batch ADMM algorithm, but without the need to visit all the samples in each iteration. Experiments on the graphguided fused lasso demonstrate that the new algorithm is signif
Shooting Stars in the Sky: An Online Algorithm for Skyline Queries
 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 ..."
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Cited by 284 (0 self)
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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
Treebased batch mode reinforcement learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the socalled Qfunction based on a set of fourtuples (xt,ut,rt,xt+1) where xt denotes the system state a ..."
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Cited by 224 (42 self)
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Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the socalled Qfunction based on a set of fourtuples (xt,ut,rt,xt+1) where xt denotes the system state
ADMM algorithm for demosaicking deblurring denoising
"... The paper is concerned with the problem of demosaicking, deblurring and denoising a color image in the same time. The global model of the acquisition chain for a color image contains these three effects, then doing restoration in the same time as demosaicking makes sense. We propose to take into acc ..."
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into account for correlation of spectral bands (R,G,B colors) by minimizing a criterion written in a nearly decorrelated basis. Then we adapt the Alternating Direction Multipliers Minimization (ADMM) method to get the solution. 1
Theory Refinement on Bayesian Networks
, 1991
"... Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory refinement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem is reduced ..."
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Cited by 255 (5 self)
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refined from data. Algorithms for refinement of Bayesian networks are presented to illustrate what is meant by "partial theory", "alternative theory representation ", etc. The algorithms are an incremental variant of batch learning algorithms from the literature so can work well
Stochastic ADMM for Nonsmooth Optimization
"... Abstract We present a stochastic setting for optimization problems with nonsmooth convex separable objective functions over linear equality constraints. To solve such problems, we propose a stochastic Alternating Direction Method of Multipliers (ADMM) algorithm. Our algorithm applies to a more gene ..."
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Abstract We present a stochastic setting for optimization problems with nonsmooth convex separable objective functions over linear equality constraints. To solve such problems, we propose a stochastic Alternating Direction Method of Multipliers (ADMM) algorithm. Our algorithm applies to a more
Asynchronous Distributed ADMM for Consensus Optimization
"... Distributed optimization algorithms are highly attractive for solving big data problems. In particular, many machine learning problems can be formulated as the global consensus optimization problem, which can then be solved in a distributed manner by the alternating direction method of multiplier ..."
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Cited by 5 (0 self)
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of multipliers (ADMM) algorithm. However, this suffers from the straggler problem as its updates have to be synchronized. In this paper, we propose an asynchronous ADMM algorithm by using two conditions to control the asynchrony: partial barrier and bounded delay. The proposed algorithm has a simple structure
Results 1  10
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2,625