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**11 - 12**of**12**### Machine Learning Algorithms for Spatio-temporal Data Mining

, 2008

"... Remote sensing, which provides inexpensive, synoptic-scale data with multi-temporal coverage, has proven to be very useful in land cover mapping, environmental monitor-ing, forest and crop inventory, urban studies, natural and man made object recognition, etc. Thematic information extracted from rem ..."

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Remote sensing, which provides inexpensive, synoptic-scale data with multi-temporal coverage, has proven to be very useful in land cover mapping, environmental monitor-ing, forest and crop inventory, urban studies, natural and man made object recognition, etc. Thematic information extracted from remote sensing imagery is also useful in vari-ety of spatiotemporal applications. However, increasing spatial, spectral, and temporal resolutions invalidate several assumptions made by the traditional classification meth-ods. In this thesis we addressed four specific problems, namely, small training samples, multisource data, aggregate classes, and spatial autocorrelation. We developed a novel semi-supervised learning algorithm to address the small training sample problem. A common assumption made in previous works is that the labeled and unlabeled training samples are drawn from the same mixture model. However, in practice we observed that the number of mixture components for labeled and unlabeled training samples differ significantly. Our adaptive semi-supervised algorithm over comes this impor-tant limitation by eliminating unlabeled samples from additional components through

### MIT Press (2000) Support Vector Method for Novelty Detection

"... Suppose you are given some dataset drawn from an underlying probabil-ity distribution P and you want to estimate a “simple ” subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified between and . We propose a method to approa ..."

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Suppose you are given some dataset drawn from an underlying probabil-ity distribution P and you want to estimate a “simple ” subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified between and . We propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a poten-tially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. We provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data. 1