Results 11  20
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509
Integrating representative and discriminant models for object category detection
 In ICCV
, 2005
"... Category detection is a lively area of research. While categorization algorithms tend to agree in using local descriptors, they differ in the choice of the classifier, with some using generative models and others discriminative approaches. This paper presents a method for object category detection w ..."
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Cited by 90 (13 self)
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Category detection is a lively area of research. While categorization algorithms tend to agree in using local descriptors, they differ in the choice of the classifier, with some using generative models and others discriminative approaches. This paper presents a method for object category detection which integrates a generative model with a discriminative
Exploring strategies for training deep neural networks
 Journal of Machine Learning Research
"... Département d’informatique et de recherche opérationnelle ..."
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Cited by 88 (12 self)
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Département d’informatique et de recherche opérationnelle
Tree induction vs. logistic regression: A learningcurve analysis
 CEDER WORKING PAPER #IS0102, STERN SCHOOL OF BUSINESS
, 2001
"... Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership pr ..."
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Cited by 85 (17 self)
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Tree induction and logistic regression are two standard, offtheshelf methods for building models for classi cation. We present a largescale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership probabilities. We use a learningcurve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several remarkable things. (1) Contrary to prior observations, logistic regression does not generally outperform tree induction. (2) More specifically, and not surprisingly, logistic regression is better for smaller training sets and tree induction for larger data sets. Importantly, this often holds for training sets drawn from the same domain (i.e., the learning curves cross), so conclusions about inductionalgorithm superiority on a given domain must be based on an analysis of the learning curves. (3) Contrary to conventional wisdom, tree induction is effective atproducing probabilitybased rankings, although apparently comparatively less so foragiven training{set size than at making classifications. Finally, (4) the domains on which tree induction and logistic regression are ultimately preferable canbecharacterized surprisingly well by a simple measure of signaltonoise ratio.
Learning Bayesian network classifiers by maximizing conditional likelihood
 In ICML2004
, 2004
"... Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when learned in the standard way. This is attributable to a mismatch between the objective function used (likelihood or a function ..."
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Cited by 84 (0 self)
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Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when learned in the standard way. This is attributable to a mismatch between the objective function used (likelihood or a function thereof) and the goal of classification (maximizing accuracy or conditional likelihood). Unfortunately, the computational cost of optimizing structure and parameters for conditional likelihood is prohibitive. In this paper we show that a simple approximation— choosing structures by maximizing conditional likelihood while setting parameters by maximum likelihood—yields good results. On a large suite of benchmark datasets, this approach produces better class probability estimates than naive Bayes, TAN, and generativelytrained Bayesian networks. 1.
Classification with Hybrid Generative/Discriminative Models
 In Advances in Neural Information Processing Systems 16
, 2003
"... Although discriminatively trained classifiers are usually more accurate when labeled training data is abundant, previous work has shown that when training data is limited, generative classifiers can outperform them. This paper describes a hybrid model in which a highdimensional subset of the p ..."
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Cited by 79 (4 self)
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Although discriminatively trained classifiers are usually more accurate when labeled training data is abundant, previous work has shown that when training data is limited, generative classifiers can outperform them. This paper describes a hybrid model in which a highdimensional subset of the parameters are trained to maximize generative likelihood, and another, small, subset of parameters are discriminatively trained to maximize conditional likelihood. We give a sample complexity bound showing that in order to fit the discriminative parameters well, the number of training examples required depends only on the logarithm of the number of feature occurrences and feature set size. Experimental results show that hybrid models can provide lower test error and can produce better accuracy/coverage curves than either their purely generative or purely discriminative counterparts. We also discuss several advantages of hybrid models, and advocate further work in this area.
Locationbased activity recognition
 In Advances in Neural Information Processing Systems (NIPS
, 2005
"... Learning patterns of human behavior from sensor data is extremely important for highlevel activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies ..."
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Cited by 79 (8 self)
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Learning patterns of human behavior from sensor data is extremely important for highlevel activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies the significant locations of a person and takes the highlevel context into account. Our system uses relational Markov networks to represent the hierarchical activity model that encodes the complex relations among GPS readings, activities and significant places. We apply FFTbased message passing to perform efficient summation over large numbers of nodes in the networks. We present experiments that show significant improvements over existing techniques. 1
Realtime compressive tracking
 In ECCV
"... Abstract. It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent ..."
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Cited by 77 (8 self)
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Abstract. It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. While much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are datadependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of selftaught learning, these misaligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from the multiscale image feature space with dataindependent basis. Our appearance model employs nonadaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is adopted to efficiently extract the features for the appearance model. We compress samples of foreground targets and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. The proposed compressive tracking algorithm runs in realtime and performs favorably against stateoftheart algorithms on challenging sequences in terms of efficiency, accuracy and robustness. 1
Conditional random fields for activity recognition
 In Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007
, 2007
"... of any sponsoring institution, the U.S. government or any other entity. ..."
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Cited by 76 (0 self)
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of any sponsoring institution, the U.S. government or any other entity.
Principled hybrids of generative and discriminative models
 In CVPR ’06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
, 2006
"... When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, for largescale applications such as object recognition, hand labelling of data is expensive, and there is much interest in semisupervised techniques ba ..."
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Cited by 75 (1 self)
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When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, for largescale applications such as object recognition, hand labelling of data is expensive, and there is much interest in semisupervised techniques based on generative models in which the majority of the training data is unlabelled. Although the generalization performance of generative models can often be improved by ‘training them discriminatively’, they can then no longer make use of unlabelled data. In an attempt to gain the benefit of both generative and discriminative approaches, heuristic procedure have been proposed [2, 3] which interpolate between these two extremes by taking a convex combination of the generative and discriminative objective functions. In this paper we adopt a new perspective which says that there is only one correct way to train a given model, and that a ‘discriminatively trained ’ generative model is fundamentally a new model [7]. From this viewpoint, generative and discriminative models correspond to specific choices for the prior over parameters. As well as giving a principled interpretation of ‘discriminative training’, this approach opens door to very general ways of interpolating between generative and discriminative extremes through alternative choices of prior. We illustrate this framework using both synthetic data and a practical example in the domain of multiclass object recognition. Our results show that, when the supply of labelled training data is limited, the optimum performance corresponds to a balance between the purely generative and the purely discriminative. 1.
The tradeoff between generative and discriminative classifiers
 Proceedings in Computational Statistics, 16th Symposium of IASC
, 2004
"... Abstract: Given any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its asymptotic error rate. We introduce a family of classifiers that interpolate the two approaches, thus providing a new way to compare them and giving an estimation ..."
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Cited by 64 (2 self)
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Abstract: Given any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its asymptotic error rate. We introduce a family of classifiers that interpolate the two approaches, thus providing a new way to compare them and giving an estimation procedure whose classification performance is well balanced between the bias of generative classifiers and the variance of discriminative ones. We show that an intermediate tradeoff between the two strategies is often preferable, both theoretically and in experiments on real data. 1