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Multiclass boosting: Theory and algorithms

Multiclass boosting: Theory and algorithms

引用

25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

作者： Saberian, Mohammad J. Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego United States

ISBN: (纸本)9781618395993

The problem of multi-class boosting is considered. A new framework, based on multi-dimensional codewords and predictors is introduced. The optimal set of codewords is derived, and a margin enforcing loss proposed. The resulting risk is minimized by gradient descent on a multidimensional functional space. Two algorithms are proposed: 1) CD-MCBoost, based on coordinate descent, updates one predictor component at a time, 2) GD-MCBoost, based on gradient descent, updates all components jointly. The algorithms differ in the weak learners that they support but are both shown to be 1) Bayes consistent, 2) margin enforcing, and 3) convergent to the global minimum of the risk. They also reduce to AdaBoost when there are only two classes. Experiments show that both methods outperform previous multiclass boosting approaches on a number of datasets.

关键词： Adaptive boosting

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Risk minimization, probability elicitation, and cost-sensitive SVMs

Risk minimization, probability elicitation, and cost-sensiti...

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27th International Conference on Machine Learning, ICML 2010

作者： Masnadi-Shirazi, Hamed Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781605589077

A new procedure for learning cost-sensitive SVM classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the cost-sensitive SVM is derived as the mini-mizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitation. These connections are generalized to cost-sensitive classification, in a manner that guar-antees consistency with the cost-sensitive Bayes risk, and associated Bayes decision rule. This ensures that optimal decision rules, under the new hinge loss, implement the Bayes-optimal cost-sensitive classification boundary. Minimization of the new hinge loss is shown to be a generalization of the classic SVM optimization problem, and can be solved by identical procedures. The resulting algorithm avoids the shortcomings of previous approaches to cost-sensitive SVM design, and has superior experimental performance. Copyright 2010 by the author(s)/owner(s).

关键词： Costs

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Asymmetric boosting 07

Asymmetric boosting

引用

24th International Conference on Machine Learning, ICML 2007

作者： Masnadi-Shirazi, Hamed Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781595937933

A cost-sensitive extension of boosting, denoted as asymmetric boosting, is presented. Unlike previous proposals, the new algorithm is derived from sound decision-theoretic principles, which exploit the statistical interpretation of boosting to determine a principled extension of the boosting loss. Similarly to AdaBoost, the cost-sensitive extension minimizes this loss by gradient descent on the functional space of convex combinations of weak learners, and produces large margin detectors. It is shown that asymmetric boosting is fully compatible with AdaBoost, in the sense that it becomes the latter when errors are weighted equally. Experimental evidence is provided to demonstrate the claims of cost-sensitivity and large margin. The algorithm is also applied to the computer vision problem of face detection, where it is shown to outperform a number of previous heuristic proposals for cost-sensitive boosting (AdaCost, CSB0, CSB1, CSB2, asymmetric-AdaBoost, AdaC1, AdaC2 and AdaC3).

关键词： Decision theory

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Variable margin losses for classifier design

Variable margin losses for classifier design

引用

24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

作者： Masnadi-Shirazi, Hamed Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781617823800

The problem of controlling the margin of a classifier is studied. A detailed analytical study is presented on how properties of the classification risk, such as its optimal link and minimum risk functions, are related to the shape of the loss, and its margin enforcing properties. It is shown that for a class of risks, denoted canonical risks, asymptotic Bayes consistency is compatible with simple analytical relationships between these functions. These enable a precise characterization of the loss for a popular class of link functions. It is shown that, when the risk is in canonical form and the link is inverse sigmoidal, the margin properties of the loss are determined by a single parameter. Novel families of Bayes consistent loss functions, of variable margin, are derived. These families are then used to design boosting style algorithms with explicit control of the classification margin. The new algorithms generalize well established approaches, such as LogitBoost. Experimental results show that the proposed variable margin losses outperform the fixed margin counterparts used by existing algorithms. Finally, it is shown that best performance can be achieved by cross-validating the margin parameter.

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On the design of loss functions for classification: Theory, robustness to outliers, and SavageBoost

On the design of loss functions for classification: Theory, ...

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22nd Annual Conference on Neural Information Processing Systems, NIPS 2008

作者： Masnadi-Shirazi, Hamed Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781605609492

The machine learning problem of classifier design is studied from the perspective of probability elicitation, in statistics. This shows that the standard approach of proceeding from the specification of a loss, to the minimization of conditional risk is overly restrictive. It is shown that a better alternative is to start from the specification of a functional form for the minimum conditional risk, and derive the loss function. This has various consequences of practical interest, such as showing that 1) the widely adopted practice of relying on convex loss functions is unnecessary, and 2) many new losses can be derived for classification problems. These points are illustrated by the derivation of a new loss which is not convex, but does not compromise the computational tractability of classifier design, and is robust to the contamination of data with outliers. A new boosting algorithm, SavageBoost, is derived for the minimization of this loss. Experimental results show that it is indeed less sensitive to outliers than conventional methods, such as Ada, Real, or LogitBoost, and converges in fewer iterations.

关键词： Specifications

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Boosting classifier cascades

Boosting classifier cascades

引用

24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

作者： Saberian, Mohammad J. Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781617823800

The problem of optimal and automatic design of a detector cascade is considered. A novel mathematical model is introduced for a cascaded detector. This model is analytically tractable, leads to recursive computation, and accounts for both classification and complexity. A boosting algorithm, FCBoost, is proposed for fully automated cascade design. It exploits the new cascade model, minimizes a Lagrangian cost that accounts for both classification risk and complexity. It searches the space of cascade configurations to automatically determine the optimal number of stages and their predictors, and is compatible with bootstrapping of negative examples and cost sensitive learning. Experiments show that the resulting cascades have state-of-the-art performance in various computer vision problems.

关键词： Classification (of information)

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A biologically plausible network for the computation of orientation dominance

A biologically plausible network for the computation of orie...

引用

24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

作者： Muralidharan, Kritika Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781617823800

The determination of dominant orientation at a given image location is formulated as a decision-theoretic question. This leads to a novel measure for the dominance of a given orientation θ, which is similar to that used by SIFT. It is then shown that the new measure can be computed with a network that implements the sequence of operations of the standard neurophysiological model of V1. The measure can thus be seen as a biologically plausible version of SIFT, and is denoted as bioSIFT. The network units are shown to exhibit trademark properties of V1 neurons, such as cross-orientation suppression, sparseness and independence. The connection between SIFT and biological vision provides a justification for the success of SIFT-like features and reinforces the importance of contrast normalization in computer vision. We illustrate this by replacing the Gabor units of an HMAX network with the new bioSIFT units. This is shown to lead to significant gains for classification tasks, leading to state-of-the-art performance among biologically inspired network models and performance competitive with the best non-biological object recognition systems.

关键词： Object recognition

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On the connections between saliency and tracking

On the connections between saliency and tracking

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26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012

作者： Mahadevan, Vijay Vasconcelos, Nuno Yahoo Labs Bangalore India Statistical Visual Computing Laboratory UC San Diego San Diego CA 92092 United States

ISBN: (纸本)9781627480031

A model connecting visual tracking and saliency has recently been proposed. This model is based on the saliency hypothesis for tracking which postulates that tracking is achieved by the top-down tuning, based on target features, of discriminant center-surround saliency mechanisms over time. In this work, we identify three main predictions that must hold if the hypothesis were true: 1) tracking reliability should be larger for salient than for non-salient targets, 2) tracking reliability should have a dependence on the defining variables of saliency, namely feature contrast and distractor heterogeneity, and must replicate the dependence of saliency on these variables, and 3) saliency and tracking can be implemented with common low level neural mechanisms. We confirm that the first two predictions hold by reporting results from a set of human behavior studies on the connection between saliency and tracking. We also show that the third prediction holds by constructing a common neurophysiologically plausible architecture that can computationally solve both saliency and tracking. This architecture is fully compliant with the standard physiological models of V1 and MT, and with what is known about attentional control in area LIP, while explaining the results of the human behavior experiments.

关键词： Forecasting

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Multiclass Boosting: Theory and Algorithms 11

Multiclass Boosting: Theory and Algorithms

引用

Annual Conference on Neural Information Processing Systems

作者： Mohammad J. Saberian Nuno Vasconcelos Statistical Visual Computing Laboratory University of California San Diego

ISBN: (纸本)9781618395993

关键词： multi-class algorithms Boost descent Global Minimum Dataset codeword

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Multi-resolution cascades for multiclass object detection 14

Multi-resolution cascades for multiclass object detection

引用

Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2

作者： Mohammad Saberian Nuno Vasconcelos Yahoo! Labs Statistical Visual Computing Laboratory University of California San Diego

An algorithm for learning fast multiclass object detection cascades is introduced. It produces multi-resolution (MRes) cascades, whose early stages are binary target vs. non-target detectors that eliminate false positives, late stages multiclass classifiers that finely discriminate target classes, and middle stages have intermediate numbers of classes, determined in a data-driven manner. This MRes structure is achieved with a new structurally biased boosting algorithm (SBBoost). SBBost extends previous multiclass boosting approaches, whose boosting mechanisms are shown to implement two complementary data-driven biases: 1) the standard bias towards examples difficult to classify, and 2) a bias towards difficult classes. It is shown that structural biases can be implemented by generalizing this class-based bias, so as to encourage the desired MRes structure. This is accomplished through a generalized definition of multiclass margin, which includes a set of bias parameters. SBBoost is a boosting algorithm for maximization of this margin. It can also be interpreted as standard multiclass boosting algorithm augmented with margin thresholds or a cost-sensitive boosting algorithm with costs defined by the bias parameters. A stage adaptive bias policy is then introduced to determine bias parameters in a data driven manner. This is shown to produce MRes cascades that have high detection rate and are computationally efficient. Experiments on multiclass object detection show improved performance over previous solutions.

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