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Algorithmic Learning in a Random World
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.
Value Added Tax Fraud
Serving as an introduction to one of the "hottest" topics in financial crime, the Value Added Tax (VAT) fraud, this new and original book aims to analyze and decrypt the fraud and explore multi-disciplinary avenues, thereby exposing nuances and shades that remain concealed by traditional taxation oriented researches. Quantifying the impact of the fraud on the real economy underlines the structural damages propagated by this crime in the European Union. The ‘fruadsters’ benefit when policy changes are inflicted in an economic space without a fully fledged legal framework. Geopolitical events like the creation of the Eurasian Union and 'Brexit' are analyzed from the perspective of the VAT ...
Postgraduate UK study and funding guide
Features information on studying at Postgraduate level in the UK, what is involved, what opportunities there are, lists details £75 million of funding available to Postgraduate students.
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