Abstract Current advances in Artificial Intelligence (AI) and Machine Learning have achieved
unprecedented impact across research communities and industry. Nevertheless, concerns …

As one of the most comprehensive machine learning texts around, this book does justice to
the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's …

Since the late 1990s predicate invention has been under-explored within inductive logic
programming due to difficulties in formulating efficient search mechanisms. However, a …

We present a prototype system, code-named Pulse, for mining topics and sentiment
orientation jointly from free text customer feedback. We describe the application of the …

Kernel methods in general and support vector machines in particular have been successful
in various learning tasks on data represented in a single table. Much'real-world'data …

Humans are often extraordinary at performing practical reasoning. There are cases where
the human computer, slow as it is, is faster than any artificial intelligence system. Are we …

Abstract Inductive Logic Programming (ILP) is an area of Machine Learning which has now
reached its twentieth year. Using the analogy of a human biography this paper recalls the …

This paper introduces a principled approach for the design of a scalable general
reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a …

In this paper we present 1BC and 1BC2, two systems that perform naive Bayesian
classification of structured individuals. The approach of 1BC is to project the individuals …

This paper brings together two strands of machine learning of increasing importance: kernel
methods and highly structured data. We propose a general method for constructing a kernel …