An application of laterally interconnected self-organizing maps (LISSOM) to handwritten digit recognition is presented. The lat(cid:173) eral connections learn the correlations of activity between units on the map. The resulting excitatory connections focus the activity into local patches and the inhibitory connections decorrelate redun(cid:173) dant activity on the map. The map thus forms internal representa(cid:173) tions that are easy to recognize with e.g. a perceptron network. The recognition rate on a subset of NIST database 3 is 4.0% higher with LISSOM than with a regular Self-Organizing Map (SOM) as the front end, and 15.8% higher than recognition of raw input bitmaps directly.

Signal processing and pattern recognition algorithms make exten(cid:173) sive use of convolution. In many cases, computational accuracy is not as important as computational speed. This form of noise justifies some level of quantization in order to achieve faster feature extraction . Our approach consists of approximating regions of the signal with low degree polynomi(cid:173) als, and then differentiating the resulting signals in order to obtain impulse functions (or derivatives of impulse functions). With this representation, convolution becomes extremely simple and can be implemented quite effectively.

There are a number of pattern recognition problem domains where the classification of an object should be based on more than simply the appearance of the object itself. In remote sensing image classification, where each pixel is part of ground cover, a pixel is more like(cid:173) ly to be a glacier if it is in a mountainous area, than if surrounded by pixels of residential areas. In text analysis, one can expect to find certain letters occurring regularly in particu(cid:173) lar arrangement with other letters(qu, ee,est, tion, etc.). The information conveyed by the accompanying entities is referred to as contextual information.

A novel approach for comparing sequences of observations using an explicit-expansion kernel is demonstrated. The kernel is derived using the assumption of the independence of the sequence of observations and a mean-squared error training criterion. The use of an explicit expan- sion kernel reduces classifier model size and computation dramatically, resulting in model sizes and computation one-hundred times smaller in our application. The explicit expansion also preserves the computational advantages of an earlier architecture based on mean-squared error train- ing. Training using standard support vector machine methodology gives accuracy that significantly exceeds the performance of state-of-the-art mean-squared error training for a speaker recognition task.

A flexible pattern-matching analog classifier is presented in con- junction with a robust image representation algorithm called Prin- cipal Axes Projection (PAP). In the circuit, the functional form of matching is configurable in terms of the peak position, the peak height and the sharpness of the similarity evaluation. The test chip was fabri- cated in a 0.6-m m CMOS technology and successfully applied to hand-written pattern recognition and medical radiograph analysis using PAP as a feature extraction pre-processing step for robust image coding. The separation and classification of overlapping patterns is also ex- perimentally demonstrated.

Previous approaches are either based on regularisation or on the gen- eration of virtual (transformed) examples. We develop a new frame- work for learning linear classifiers under known transformations based on semidefinite programming. We present a new learning algorithm-- the Semidefinite Programming Machine (SDPM)--which is able to find a maximum margin hyperplane when the training examples are polynomial trajectories instead of single points. The solution is found to be sparse in dual variables and allows to identify those points on the trajectory with minimal real-valued output as virtual support vec- tors. Extensions to segments of trajectories, to more than one trans- formation parameter, and to learning with kernels are discussed.

Recently, there have been several advances in the machine learning and pattern recognition communities for developing manifold learning algo- rithms to construct nonlinear low-dimensional manifolds from sample data points embedded in high-dimensional spaces. In this paper, we de- velop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the neighborhood sizes; and 2) better fitting the local geometric structure to account for the variations in the curvature of the manifold and its interplay with the sampling density of the data set. We also illustrate the effectiveness of our methods on some synthetic data sets.

In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incor- porating knowledge on the structure of the data into base classifier design and selection. On the other hand, we use ADABOOST's efficient learn- ing mechanism to significantly improve supervised and semi-supervised algorithms proposed in the context of manifold learning. Beside the spe- cific manifold-based penalization, the resulting algorithm also accommo- dates the boosting of a large family of regularized learning algorithms. ADABOOST [1] is one of the machine learning algorithms that have revolutionized pattern recognition technology in the last decade.

In this paper we consider the problem of finding sets of points that conform to a given underlying model from within a dense, noisy set of observations. This problem is motivated by the task of efficiently linking faint asteroid detections, but is applicable to a range of spatial queries. We survey current tree-based approaches, showing a trade-off exists between single tree and multiple tree algorithms. To this end, we present a new type of multiple tree algorithm that uses a variable number of trees to exploit the advantages of both approaches. We empirically show that this algorithm performs well using both simulated and astronomical data.

We investigate the learning of the appearance of an object from a single image of it. Instead of using a large number of pictures of the object to recognize, we use a labeled reference database of pictures of other ob- jects to learn invariance to noise and variations in pose and illumination. This acquired knowledge is then used to predict if two pictures of new objects, which do not appear on the training pictures, actually display the same object. We propose a generic scheme called chopping to address this task. It relies on hundreds of random binary splits of the training set chosen to keep together the images of any given object.