## Abstracts## Estimated margin data for Boosting and SVM
In large margin algorithms such as boosting and SVM, training data that lie close to decision frontiers play a crucial role. We estimate the distribution of these “margin” training patterns and empirically investigate how to use it to cope with large data sets in boosting and SV machines. We principally focus on the understanding of boosting and then give some first results about SV machines. First we show that the estimated distribution of margin training data can be injected as initial distribution to be learnt in boosting algorithm. This considerably reduces complexity maintaining the high level of performance. Second, while using boosting, it appears as an empirical evidence that both first and last computed experts play a crucial role. The first expert is specialized on the easy-to-learn data provided a global trend for the decision. The second expert appears to be specialized in hard-to-learn data providing a local detailed decision. Thus a possible way to simplify boosting for large data sets consists in combining two hypothesis, one trained on the hard-to-learn training data and the other trained on the easy-to-learn training data. Of course, these two classifiers must have sufficient capacity to solve the two problems. This is the case for large enough MLPs. The application of this new method on several data sets illustrates its relevance. Finally, since most of hard-to-learn data lie in the margins of the classifiers, the estimation of this distribution can be used to provide a set of candidates support vectors. This set can be efficiently used to initialize a SVM algorithm implementation. Joint work with M.-R. Amini, Laboratoire d'Informatique de Paris 6, Paris, and Stephane Canu, PSI, INSA de Rouen, Mont Saint-Aignant. ## Error Estimates for Large Margin Classifiers
We give upper and lower bounds on misclassification probability for large margin classifiers. For any class of thresholded real-valued functions satisfying a mild uniformity condition, achieving misclassification probability nearly as small as that of the best large margin classifier in the class requires an amount of data that grows with the logarithm of a certain covering number of the class. We present a number of applications of these results. Nearest neighbour classification and large margins Jonathan Baxter, Austrailan National University, Department of Systems Engineering Nearest neighbour methods are among the simplest and most widely used pattern classification techniques. Recent analysis has shown that the generalisation performance of classifiers optained by thresholding real-valued functions is intimately connected with the size of the “margins” of the training examples. In this talk I will present some experiments with a character classification problem showing that nearest neighbour algorithms also exhibit an association between the “margin” and the generalisation error. Some of the theory relevant to analysing this phenomenon will be discussed. ## Enlarging the Margins in Perceptron Decision Trees
Capacity control in Perceptron Decision Trees is typically performed by controlling their size. We have proven that other quantities can be as relevant to reduce their flexibility and combat overfitting. In particular, we derived an upper bound on the generalization error which depends both on the size of the tree and on the margin of the decision nodes. We now examine how these theoretical results can be incorporated into practical algorithms. We study three possible approaches all using the perceptron decision tree algorithm, OC1, as a baseline. OC1 is a top-down decision tree algorithm that optimizes each linear decision using a randomized search. The first algorithm, FAT, starts with the tree constructed by OC1 and constructs the tree with the hyperplanes of each OC1 node replaced by the maximum margin hyperplane implementing the same decision. The second approach, Margin OC1 (MOC1), alters the OC1 splitting criterion to minimize the original OC1 splitting criterion and maximize the decision margin at each node. Both FAT and MOC1 are hard margin approaches. The margin is the distance between the two closest points on either side of the decision hyperplane. The final algorithm utilizes soft margins in much the same manner as Support Vector Machines. In order to achieve larger margins, points are allowed to fall within the margin. The splitting criterion is modified to only count the points falling outside the margin as being correctly split. An extensive experimental study on real world data confirms our approach. Individually the margin-based approaches compared favorably to the unaltered OC1 algorithm. Each algorithm performs better or at least not significantly worse then OC1 on almost every dataset. OC1 did significantly worse than the best margin-based method on every dataset except one. Joint work with Nello Cristianini, Dept. of Engineering Mathematics,University of Bristol, John Shawe-Taylor, Dept. of Computer Science, Royal Holloway, University of London, and Donghui Wu, Department of Mathematical Sciences, Rensselaer Polytechnic Institute ## Are margins relevant in voting?
First, I'll present evidence that in the case of arcing classifiers, higher margins do not imply lower generalization error. Then, I'll present a new combination method called half&half bagging which gets results competitive with Adaboost, but is of an entirely different type. Looking at half&half bagging, one can see more tranparently what is going on. The successful combination methods seem to be trying to find the analogue of the support vectors and fasten the boundary to these. It is not at all clear what the relevance of the margins is. Empirical Risk Approximation for Unsupervised Learning Joachim Buhmann, University of Bonn Unsupervised learning algorithms are designed to extract structure from data without reference to explicit teacher information. The quality of the learned structure is determined by a cost function which guides the learning process. This paper proposes EMPIRICAL RISK APPROXIMATION as a new induction principle for unsupervised learning. The complexity of the unsupervised learning models are automatically controlled by large deviation bounds. The maximal entropy principle with deterministic annealing as an efficient search strategy arises from the EMPIRICAL RISK APPROXIMATION principle as the optimal inference strategy for large learning problems. Parameter selection of learnable data structures is demonstrated for the case of K-means and distributional clustering. ## Discriminative Gaussian Mixture Models
Gaussian mixture models are commonly used for the speaker identification task. A model, or set of models, is built for each speaker, and maximum likelihood used to identify the speaker given the data. This method is not inherently discriminative: a model for a given speaker is built using data from only that speaker. In this talk I will discuss one method for adding discrimination to Gaussian mixture models using support vector machines, and give results on a NIST speaker identification task. ## Margin Distribution Bounds on Generalization
A number of results have bounded generalization of a classifier in terms of its margin on the training points. There has been some debate about whether the minimum margin is the best measure of the distribution of training set margin values with which to estimate the generalization. Freund and Schapire and Shapire, COLT98 have shown how a different function of the margin distribution can be used to bound the number of mistakes of an on-line learning algorithm for a perceptron, as well as an expected error bound. We show that a slight generalization of their construction can be used to give a new pac style bound on the tail of the distribution of the generalization errors that arise from a given sample size. Our bound involves the sum of the squares of the distances by which points fail to meet a specified margin. For the maximal margin this sum is zero and the bound reduces to the standard one. For larger margins or classifiers that have training errors the bounds can be significantly better than previous ones. The analysis also suggests a different criterion to optimise when training a linear classifier. We present a polynomial algorithm for a modified Kernel based linear machine which directly minimizes this criterion. This algorithm handles training errors optimally with respect to the computed generalization error bound in contrast to previous approaches to handling errors which are heuristic. COLT98 Yoav Freund and Robert E. Schapire, Large Margin Classification Using the Perceptron Algorithm, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, 1998. Joint work with John Shawe-Taylor, Dept. of Computer Science, Royal Holloway, University of London ## Large Margin Classification Using the Perceptron Algorithm
We introduce and analyze a new algorithm for linear classification which combines Rosenblatt's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large margins. Compared to Vapnik's algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our algorithm, and some variants of it, for classifying images of handwritten digits. The performance of our algorithm is close to, but not as good as, the performance of maximal-margin classifiers on the same problem. Joint work with Robert E. Schapire, AT&T Research ## Support Vector Neural Networks
The kernel Adatron with bias and soft margin (KAb) is a new neural network alternative to support vector (SV) machines. It can learn large-margin decision functions in kernel feature spaces in an iterative “on-line” fashion which are similar to support vector machines. In contrast support vector learning is batch learning and is strongly based on solving constrained quadratic programming problems. Quadratic programming is nontrivial to implement and can be subject to stability problems. The kernel Adatron algorithm (KA) has been introduced recently. So far it has been assumed that the bias parameter of the plane in feature space is always zero, and that all patterns can be correctly classified by the learning machine. These assumptions cannot always be made. The kernel Adatron with bias and soft margin (KAb) combines the speed and simplicity of a neural network with the predictive power of SV machines. However the KAb does not, unlike to SV machines, suffer from any problems related to quadratic programming, and unlike to conventional neural networks the KAb's cost function is always convex such that it is guraenteed that the global optimum of the cost function will be found during the learning process. The kernel Adatron with bias and soft margin is introduced and discussed, then experimental studies using benchmarks and real data are presented which allow to compare the performance of kernel Adatrons and SV machines. ## A note on a scale-sensitive dimension of linear bounded functionals in Banach spaces
We show that 'B is of type p > 1’ is a necessary and sufficient condition for learnability of a class of linear bounded functionals with norm <= 1 restricted to the unit ball in Banach Space B. On the way we give a very short probabilistic proof for Vapnik's result (Hilbert Space and improved) and imrove our result with Pascal Koiran for convex hulls of indicator functions. The approach we use in this paper allows to connect various results about learnability and approximation. ## What linear discriminant techniques teach us about SVMs
Support Vector Machines were introduced and first applied to classification problems as alternatives to multi-layer neural networks. The high generalization ability provided by the Optimum Margin Classifier SVM, in particular, has inspired work on computational speedups as well as the fundamental theory of model complexity and generalization. At first glance, SVM classifiers appear to be nothing more than a generalized linear discriminant in a high-dimensional transformed feature space; indeed, many aspects of SVM classifiers can best be understood in relation to traditional linear discriminant techniques. However, SVM classifiers are more powerful than such traditional methods, in part because the natural way in which complexity can be adjusted in SVMs. The theory of SVMs sheds new light on the “curse of dimensionality,” in particular, the extent to which it is truly a “curse.” There are, moreover, relationships between the training algorithms of classical linear classifiers and those of SVMs that lead to new training methods for both types of classifier. Our presentation will include: A modification to the Perceptron algorithm that gives a simple on-line training method for SVMs. A deeper understanding of the information theoretic novelty of support vectors (formerly called informative patterns). How to train SVMs with linear programming using the simplex method. How the “dual space representation” employed in SVMs can be used in other algorithms such as the Perceptron, Potential functions, and Pseudo-Inverse methods. Why “soft margins” are needed and why non-separable problems cannot be addressed by training SVM classifiers to have negative margins.
Joint work with David Stork, CRC, Ricoh ## Large Margin Rank Boundaries for Preference Learning
In this talk we investigate the problem of learning a preference relation from a given set of ranked objects. The main difference between preference learning and standard classification learning is the demand, that the mapping from objects to ranks has to be transitive and asymmetric. To model this problem we present an approach that performs a linear mapping from objects to scalar utility values and thus guarantees transitivity and asymmetry. The learning of the preference between objects is formulated as a classification problem on pairs of objects and is solved by maximizing the margin at the rank boundaries defined on the utility function. The approach is extended to nonlinear utility functions by using the potential function method (the so called kernel trick), which allows to incorporate higher order correlations of features into the utility function at minimal computational costs. We will present some application in Information Retrieval tasks. Joint work with Thore Graepel, University of Technology Berlin ## Boosting Regressors
There is growing interest in extending the boosting algorithm to regression problems. In this paper, we develop a boosting algorithm that incrementally builds a committee of independent “weak” regressors by dynamically adjusting the margin of the committee and finding the combining coefficients that minimize training error. The algorithm is compared against bagging and support vector regression machines on artificial and real-world datasets. Joint work with John Shawe-Taylor, Dept. of Computer Science, Royal Holloway, University of London ## Local training of Support Vector Machines
For a separable data set, a local learning rule (a modification of classical perceptron) will be presented. This modification of the classical perceptron generates a series of approximations of the ideal solution, with each update requiring a knowledge of a current approximation and of the tested training instance. The algorithm is proved to generate in iterations a hyperplane separating a finite subset of a Euclidean space with a margin larger than , where and is the maximal margin. Another, a soft margin algorithm designed to deal with a (linearly) non-separable data set, will also be discussed. It uses pairs of input points for updates of the hyperplane. In each iteration it decreases the primal (soft margin) cost function and it is able to iterate unless the desired solution (the global minimum) is reached. Joint work with Trevor Anderson, Telstra Research Laboratories ## Mathematical Programming in Machine Learning
The role of mathematical programming in supervised and unsupervised machine learning will be outlined. Beginning with some early work, where linear programming was used to generate large margin linear and nonlinear classifiers, to more recent linear programming approaches to massive data discrimination via support vector machines, the role of mathematical programming will be highlighted. Other problems addressed as concave function minimization on a polytope include: feature selection, k-median clustering and parsimonious least-norm approximation. Theoretical results justifying these approaches will be presented together with computational results on publicly available data sets. ## -AdaBoost Classification
Recently ensemble methods like AdaBoost were successfully applied to character recognition tasks, seemingly defying the problems of overfitting. Central for understanding this fact is the margin distribution and we find that AdaBoost achieves – doing gradient descent in an error function with respect to the margin – asymptotically a hard margin distribution. The algorithm asymptotically concentrates its resources on a few hard-to-learn patterns. We call them Support Patterns (SP) in analogy to Support Vectors (SV). We describe a new soft margin AdaBoost algorithm, with a parameter Joint work with Klaus-Robert Müler, Bernhard Schökopf, and Alex J. Smola, GMD FIRST, Berlin ## Statistical Mechanics of Support Vector Learning
Using methods of Statistical Mechanics, we calculate the average case learning curves for SV classifiers in the limit of large input dimension N (and nice input distributions) within a teacher-student scenario. We assume kernels of the form K(x,y)=f(x Joint work with Rainer Dietrich, University Würzburg ## Making Support Vector Machines Yield Probabilities
The output of a classifier should be a calibrated posterior probability, in order to do further processing on it. Standard SVMs do not provide such probabilities. One method to create probabilities is to train a system with a squashing function that maps raw output into probabilities. Such a system is typically trained with a KL or maximum likelihood score. However, it can be shown that no such squashing function will map the KL error metric into the SVM cost function. Instead, we train an SVM, then train the parameters of an additional squashing function to map the SVM outputs into probabilities. These trained output probabilities seem to work well: I will present a comparison between SVM output probabilities and standard techniques (such as logistic regression) on a few data sets. ## Computing the Bayes Support Vector Machine
A method inspired by ergodic billiards has been recently applied to compute the Bayes Perceptron. In this talk we extend this method to support vector machines. We show that this simple billiard algorithm constructs a Gaussian kernel SVM arbitrarily close to the Bayes classifier. For real data sets this algorithm provides solutions at least as good as maximal margin SVM's. Joint work with Mario Marchand, SITE, University of Ottawa ## Reparametrizing SV Machines
We describe a new Support Vector classification algorithm, with a parameter which essentially controls the number of Support Vectors and the number of points lying inside the margin. The introduction of has the additional benefit of enabling us to eliminate the regularization constant C from the algorithm. Joint work with Alex Smola, GMD FIRST, Berlin and Robert Williamson, Dept. of Engineering, Australian National University ## General linear ensemble methods for learning
Recently there has been tremendous interest in linear methods for learning classifiers. Linear ensemble methods such as boosting, Bayesian model averaging, and bagging all learn aggregate classifiers that are linear weighted votes of basis classifiers. Maximum margin algorithms, too, produce linear weighted votes over basis classifiers (albeit implicitly defined in the case of kernel classifiers). The family of such linear learning algorithms is in fact quite rich. However, there is a fundamental distinction in this family between “convergent” methods (Bayes, bagging) which asymptotically concentrate their weight on a single best component (basis classifier) with increasing sample size, and “divergent” methods (boosting, maximum margin algorithms) which asymptotically distribute their vote over a nontrivial combination of basis classifiers. This fundamental difference gives rise to clearly distinctive properties in experiments: convergent methods have strong variance reducing but weak bias reducing properties, whereas divergent methods have the opposite strengths and weaknesses. In this work I present new methods that adaptively choose between these two behaviors (ultimately) — based on comparing the best single component to the best linear combination. Preliminary results show that these algorithms can be more robust against the full range of overfitting and underfitting conditions than previous methods. (However, achieving results which are uniformly competitive against the best method under each condition remains open.) ## Boosted Vector Machines
We describe an efficient implementation of AdaBoost for vector machines using real-valued predictions. We give several schemes for identifying candidates for weak-learners and describe an efficient Newton-based technique for finding the parameters of boosted vector machines (BVM). Finally, we discuss the results of empirical evaluation of BVM and SVM. ## Entropy Numbers, Operators, and Kernels
We derive new bounds for the generalization error of kernel machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs make use of a viewpoint that is apparently novel in the field of statistical learning theory. The hypothesis class is described in terms of a linear operator mapping from a possibly infinite dimensional unit ball in feature space into a finite dimensional space. The covering numbers of the class are then determined via the entropy numbers of the operator. These numbers, which characterize the degree of compactness of the operator, can be bounded in terms of the eigenvalues of an integral operator induced by the kernel function used by the machine. As a consequence we are able to theoretically explain the effect of the choice of kernel function on the generalization performance of support vector machines and linear programming machines, i.e. algorithms exploiting convex combinations of kernel functions. Joint work with Robert Williamson, Dept. of Engineering, Australian National University and Bernhard Schökopf, GMD FIRST, Berlin ## Error Bounds for Support Vector Machines
Two types of error bounds are considered: Bounds for fixed margin classifiers for which regular VC bounds are valid (in these bounds the VC dimension is evaluated using the margin) and Bounds for maximal margin classifiers which depend on expectation of the ratio of two values the radius of the sphere containing the support vectors to the margin.
The problem of the reason of generalization will be discussed. ## A look at Structural Risk Minimization, Generalized Approximate Cross Validation, and the Generalized Degrees of Freedom for Signal
We take a look at the relationships between these three approaches to model selection/tuning from the point of view of the class of models which can be cast as solutions to variational problems in regulariazation form. ## Hinge Loss and Average Margins for Predicting with Linear Threshold Functions with Applications to Boosting
We describe a unifying framework for proving relative loss bounds for on-line learning algorithms that use linear threshold functions for classifying the examples. The most notable examples of such algorithms are the Perceptron algorithm and the Winnow algorithm. For classification problems the discrete loss is used, i.e., the total number of prediction mistakes. We introduce a continuous loss function called the ‘‘hinge loss’’ that can be used to derive the updates of the algorithms. Our method consists of first proving relative loss bounds w.r.t. the continuous hinge loss and then converting these bounds to the discrete loss. We introduce a notion of ‘‘average margin’’ of a set of examples relative to a linear threshold classifier. We show how the relative loss bounds i.t.o. the continuous hinge loss can be converted to relative loss bounds i.t.o. the discrete loss using the average margin. Previous bounds for linear thresholded classification algorithms, such as those based on notions of ‘‘deviation’’ and ‘‘attribute error’’ are shown to be suboptimal when compared to the corresponding bounds based on the average margin. Using the average margin we show new bounds for various on-line classification algorithms, such as the -norm Perceptron and normalized Winnow algorithm. We will also show preliminary work for applying the same methods to the boosting setting. Again the analysis is first done with respect to a continous loss that motivates the updates of the boosting algorithms. The continous loss bounds are then converted to the discrete loss using an average margin. Joint work with Claudio Gentile, UC Santa Cruz ## Predictive power of bounds and stacking support vector machines
We present an investigation into the effectiveness of model selection, using various bounds obtained for the generalization ability of Support Vector Machines (SVMs) as a selection criteria. We compare the traditional model selection methods (the leave one out bound and the VC bound based on the ratio of the minimal enclosing sphere of the training data and the margin) to to a recently proposed bound suggested by Vapnik which restricts the sphere to the support vectors. A method for combining SVMs similar to stacking is also proposed. This technique is compared to the model selection methods above for various sets of SVMs on different datasets. The stacking method proposed is shown to exceed the performance of the best bounding method. Joint work with Craig Saunders, Royal Holloway London ## Mean Field Algorithms for Gaussian Process Classification and Support Vector Machines
It is known that there exits a structural similarity between the Bayesian approach to learning using Gaussian Processes and Support Vector Machines. In the talk, this relationship will be discussed for the case of a mean field approach to classification with Gaussian processes. Although the algorithms from the outset have very different objectives, the solutions obtained are quite similar and with similar performance on benchmark data sets. The structural similarity may furthermore be exploited to derive a new built-in leave-one-out estimator for the generalization error of the Support Vector Machine. Joint work with Manfred Opper, NCRG Aston ## Automatic Target Recognition with Support Vector Machines
Automatic target recognition (ATR) systems seek to classify specific classes of objects from an universe that is larger than the objects used in the training set (called confusers). In this paper, the ATR experiments are performed for synthetic aperture radar (SAR) imagery using vehicles in the public domain MSTAR database. To get reasonable recognition performance, we need a classifier with good generalization ability, i.e., to classify the targets themselves as well as their variants (different serial numbers), and to reject confusers, all at a reasonable level. In this paper we choose the support vector machine because it is a large margin classifier . The kernel Adatron with bias and soft margin (KAb)(recently developed by T. Friess) is a simple and fast method to solve the quadratic programming problem. In our experiment, the Gaussian kernel is tested and it provides exciting performance. As a comparsion, two other feature extraction methods are also employed, i.e., the information theory learning (ITL) and multi-resolution PCA. Our experiments show that support vector machine are a promising alternative for SAR ATR. Joint work with Jose Principe, University of Florida, Gainesville |