Simon Perkins

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We present a novel and flexible approach to the problem of feature selection, called grafting. Rather than considering feature selection as separate from learning, grafting treats the selection of suitable features as an integral part of learning a predictor in a regularized learning framework. To make this regularized learning process sufficiently fast for(More)
In this paper, we present a new framework for online novelty detection on temporal sequences. This framework include a mechanism for associating each detection result with a confidence value. Based on this framework, we develop a concrete online detection algorithm, by modeling the temporal sequence using an online support vector regression algorithm.(More)
Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector(More)
In the standard feature selection problem, we are given a fixed set of candidate features for use in a learning problem, and must select a subset that will be used to train a model that is “as good as possible” according to some criterion. In this paper, we present an interesting and useful variant, the online feature selection problem, in which, instead of(More)
We present a framework for real-time view-dependent refinement, and adapt it to the task of browsing large model repositories on the Internet. We introduce a novel hierarchical representation of atomic operations based on a graph structure, and provide a correspondence between the nodes of this hierarchy and a spatial representation of these operations,(More)
HIV-1/AIDS vaccines must address the extreme diversity of HIV-1. We have designed new polyvalent vaccine antigens comprised of sets of 'mosaic' proteins, assembled from fragments of natural sequences via a computational optimization method. Mosaic proteins resemble natural proteins, and a mosaic set maximizes the coverage of potential T-cell epitopes(More)
We consider the problem of pixel-by-pixel classification of a multi-spectral image using supervised learning. Conventional supervised classification techniques such as maximiun likelihood classifcation and less conventional ones such as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see(More)
We have developed an automated feature detection/classification system, called GENetic Imagery Exploitation (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENIE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in(More)
The “pixel purity index” (PPI) algorithm proposed by Boardman, et al. identifies potential endmember pixels in multispectral imagery. The algorithm generates a large number of “skewers” (unit vectors in random directions), and then computes the dot product of each skewer with each pixel. The PPI is incremented for those pixels associated with the extreme(More)