Learning for the internet : kernel embeddings and optimisation
In this thesis, we develop principled machine learning methods suited for complex real-world Internet challenges. The Internet has supplied an unprecedented amount of data; the challenge now is to transform this massive amount of data into information that supports knowledge creation. Machine learning techniques have become prevalent for modelling, prediction, and decision making from massive scale data. This thesis makes contributions in addressing data to knowledge transformation in the...[Show more]
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