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Kernel methods make it relatively easy to define complex high-dimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When two views of the same phenomenon are available kernel Canonical Correlation Analysis (KCCA) has been shown to be an effective preprocessing step that can improve(More)
The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes , bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide(More)
• The Learning for adaptive visual agents project, led by Prof. John Shawe-Taylor, focuses on applying machine learning techniques to problems in machine vision. I am investigating the combination of generative probabilistic models (e.g. GMMs) with discriminative classifiers (e.g. SVMs) and applying them to object recognition problems. • My thesis, Rational(More)
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