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Generalized Discriminant Analysis Using a Kernel Approach

by G. Baudat, F. Anouar , 2000
"... We present a new method that we call Generalized Discriminant Analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is close to the Support Vector Machines (SVM) insofar as the GDA method provides a mapping of the input vectors into high di ..."
Abstract - Cited by 333 (2 self) - Add to MetaCart
We present a new method that we call Generalized Discriminant Analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is close to the Support Vector Machines (SVM) insofar as the GDA method provides a mapping of the input vectors into high dimensional feature space. In the transformed space, linear properties make it easy to extend and generalize the classical Linear Discriminant Analysis (LDA) to non linear discriminant analysis. The formulation is expressed as an eigenvalue problem resolution. Using a different kernel, one can cover a wide class of nonlinearities. For both simulated data and alternate kernels, we give classification results as well as the shape of the separating function. The results are confirmed using a real data to perform seed classification. 1. Introduction Linear discriminant analysis (LDA) is a traditional statistical method which has proven successful on classification problems [Fukunaga, 1990]. The p...

Who should fix this bug?

by John Anvik, et al. - ICSE'06 , 2006
"... Open source development projects typically support an open bug repository to which both developers and users can report bugs. The reports that appear in this repository must be triaged to determine if the report is one which requires attention and if it is, which developer will be assigned the respo ..."
Abstract - Cited by 231 (8 self) - Add to MetaCart
Open source development projects typically support an open bug repository to which both developers and users can report bugs. The reports that appear in this repository must be triaged to determine if the report is one which requires attention and if it is, which developer will be assigned the responsibility of resolving the report. Large open source developments are burdened by the rate at which new bug reports appear in the bug repository. In this paper, we present a semi-automated approach intended to ease one part of this process, the assignment of reports to a developer. Our approach applies a machine learning algorithm to the open bug repository to learn the kinds of reports each developer resolves. When a new report arrives, the classifier produced by the machine learning technique suggests a small number of developers suitable to resolve the report. With this approach, we have reached precision levels of 57 % and 64 % on the Eclipse and Firefox development projects respectively. We have also applied our approach to the gcc open source development with less positive results. We describe the conditions under which the approach is applicable and also report on the lessons we learned about applying machine learning to repositories used in open source development.
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Content-Based Audio Classification and Retrieval by Support Vector Machines

by Guodong Guo, Stan Z. Li , 2000
"... Support vector machines (SVMs) have been recently proposed as a new learning algorithm for pattern recognition. In this paper, the SVMs with a binary tree recognition strategy are used to tackle the audio classification problem. We illustrate the potential of SVMs on a common audio database, which c ..."
Abstract - Cited by 67 (1 self) - Add to MetaCart
Support vector machines (SVMs) have been recently proposed as a new learning algorithm for pattern recognition. In this paper, the SVMs with a binary tree recognition strategy are used to tackle the audio classification problem. We illustrate the potential of SVMs on a common audio database, which consists of 409 sounds of 16 classes. We compare the SVMs based classification with other popular approaches. For audio retrieval, we propose a new metric, called distance-from-boundary (DFB). When a query audio is given, the system first finds a boundary inside which the query pattern is located. Then, all the audio patterns in the database are sorted by their distances to this boundary. All boundaries are learned by the SVMs and stored together with the audio database. Experimental comparisons for audio retrieval are presented to show the superiority of this novel metric to other similarity measures.

An SVM learning approach to robotic grasping

by Raphael Pelossof, Andrew Miller, Peter Allen, Tony Jebara - In IEEE International Conference on Robotics and Automation , 2004
"... Abstract — Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degrees-of-freedom and geometry of the object to be grasped creates a high-dimensional, non ..."
Abstract - Cited by 61 (8 self) - Add to MetaCart
Abstract — Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degrees-of-freedom and geometry of the object to be grasped creates a high-dimensional, non- smooth manifold. Traditional search methods applied to this manifold are typically not powerful enough to find appropriate stable grasping solutions, let alone optimal grasps. We address this issue in this paper, which attempts to find optimal grasps of objects using a grasping simulator. Our unique approach to the problem involves a combination of numerical methods to recover parts of the grasp quality surface with any robotic hand, and contemporary machine learning methods to interpolate that surface, in order to find the optimal grasp. I.
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Image-based human age estimation by manifold learning and locally adjusted robust regression

by Guodong Guo, Senior Member, Yun Fu, Student Member, Charles R. Dyer, Thomas S. Huang, Life Fellow - IEEE Transactions on Image Processing , 2008
"... Abstract—Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively e ..."
Abstract - Cited by 59 (5 self) - Add to MetaCart
Abstract—Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person’s gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database. Index Terms—Age manifold, human age estimation, locally adjusted robust regression, manifold learning, nonlinear regression, support vector machine (SVM), support vector regression (SVR). I.

Coping with an open bug repository

by John Anvik, Lyndon Hiew, Gail C. Murphy - In OOPSLA workshop on Eclipse technology eXchange , 2005
"... Most open source software development projects include an open bug repository—one to which users of the software can gain full access—that is used to report and track problems with, and potential enhancements to, the software system. There are several potential advantages to the use of an open bug r ..."
Abstract - Cited by 58 (4 self) - Add to MetaCart
Most open source software development projects include an open bug repository—one to which users of the software can gain full access—that is used to report and track problems with, and potential enhancements to, the software system. There are several potential advantages to the use of an open bug repository: more problems with the system might be identified because of the relative ease of reporting bugs, more problems might be fixed because more developers might engage in problem solving, and developers and users can engage in focused conversations about the bugs, allowing users input into the direction of the system. However, there are also some potential disadvantages such as the possibility that developers must process irrelevant bugs that reduce their productivity. Despite the rise in use of open bug repositories, there is little data about what is stored inside these repositories and how they are used. In this paper, we provide an initial characterization of two open bug repositories from the Eclipse and Firefox projects, describe the duplicate bug and bug triage problems that arise with these open bug repositories, and discuss how we are applying machine learning technology to help automate these processes.
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Content-Based Audio Classification and Retrieval Using SVM Learning

by Stan Z. Li , GuoDong Guo
"... In this paper, a support vector machines (SVMs) based method is proposed for content-based audio classification and retrieval. Given a feature set, which in this work is composed of perceptual and cepstral feature, optimal class boundaries between classes are learned from training data by using SVMs ..."
Abstract - Cited by 55 (1 self) - Add to MetaCart
In this paper, a support vector machines (SVMs) based method is proposed for content-based audio classification and retrieval. Given a feature set, which in this work is composed of perceptual and cepstral feature, optimal class boundaries between classes are learned from training data by using SVMs. Matches are ranked by using distances from boundaries. Experiments are presented to compare various classification methods and feature sets.

Kernels as Features: On Kernels, Margins, and Low-dimensional Mappings

by Maria-florina Balcan, Avrim Blum, Santosh Vempala - In 15th International Conference on Algorithmic Learning Theory (ALT ’04 , 2004
"... Kernel functions are typically viewed as providing an implicit mapping of points into a high-dimensional space, with the ability to gain much of the power of that space without incurring a high cost if the result is linearly-separable by a large margin #. However, the JohnsonLindenstrauss lemma ..."
Abstract - Cited by 47 (6 self) - Add to MetaCart
Kernel functions are typically viewed as providing an implicit mapping of points into a high-dimensional space, with the ability to gain much of the power of that space without incurring a high cost if the result is linearly-separable by a large margin #. However, the JohnsonLindenstrauss lemma suggests that in the presence of a large margin, a kernel function can also be viewed as a mapping to a low-dimensional space, one of dimension only O(1/# ). In this paper, we explore the question of whether one can e#ciently produce such low-dimensional mappings, using only black-box access to a kernel function. That is, given just a program that computes K(x, y) on inputs x, y of our choosing, can we e#ciently construct an explicit (small) set of features that e#ectively capture the power of the implicit high-dimensional space? We answer this question in the a#rmative if our method is also allowed black-box access to the underlying data distribution (i.e., unlabeled examples). We also give a lower bound, showing that if we do not have access to the distribution, then this is not possible for an arbitrary black-box kernel function; we leave as an open problem, however, whether this can be done for standard kernel functions such as the polynomial kernel.
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Modeling Drivers' Speech Under Stress

by Raul Fernandez, Rosalind W. Picard , 2000
"... In this paper we explore the use of features derived from multiresolution analysis of speech and the Teager Energy Operator for classification of drivers' speech under stressed conditions. We apply this set of features to a database of short speech utterances to create userdependent discriminan ..."
Abstract - Cited by 39 (2 self) - Add to MetaCart
In this paper we explore the use of features derived from multiresolution analysis of speech and the Teager Energy Operator for classification of drivers' speech under stressed conditions. We apply this set of features to a database of short speech utterances to create userdependent discriminants of four stress categories. In addition we address the problem of choosing a suitable temporal scale for representing categorical differences of the data. This leads to two sets of modeling techniques. In the first approach, we model the dynamics of the feature set within the utterance with a family of dynamic classifiers. In the second approach, we model the mean value of the features across the utterance with a family of static classifiers. We report and compare classiftcation performances on the sparser and full dynamic representations for a set of four subjects.

Travel-Time Prediction with Support Vector Regression

by Chun-hsin Wu, Jan-ming Ho, D. T. Lee - IEEE Transactions on Intelligent Transportation Systems , 2004
"... Abstract—Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. In this paper, we apply support vector regression (SVR) for travel-time prediction and ..."
Abstract - Cited by 37 (0 self) - Add to MetaCart
Abstract—Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. In this paper, we apply support vector regression (SVR) for travel-time prediction and compare its results to other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can significantly reduce both relative mean errors and root-mean-squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and performs well for traffic data analysis. Index Terms—Intelligent transportation systems (ITSs), support vector machines, support vector regression (SVR), time series analysis, travel-time prediction. I.
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