Information theoretic combination of pattern classifiers

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One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher-student perceptron on i.i.d. Gaussian inputs. Both Bayes-optimal (BO) ...

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Information is the resolution of uncertainty and manifests itself as patterns. Although complex, most observable phenomena are not random and instead are associated with deterministic, chaotic systems. The underlying patterns and symmetries expressed from ...

Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity. Their reliance on pre-defined distance metrics-e.g. the Euclidean or Manhattan distance-are problematic especially when used in c ...

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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Information theoretic combination of pattern classifiers

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