Current machine learning (ML) algorithms identify statistical regularities in complex data sets and are regularly used across a range of application domains, but they lack the robustness and generalizability associated with human learning. If ML techniques could enable computers to learn from fewer examples, transfer knowledge between tasks, and adapt to changing contexts and environments, the ...

The last decade has seen considerable growth in interest in Artificial Intelligence and Machine Learning. In the broadest sense, these fields aim to ‘learn something useful’ about the environment within which the organism operates. How gathered information is processed leads to the development of algorithms, how to process high dimensional data and deal with uncertainty. In the early stages of ...

SystemML aims at declarative, large-scale machine learning (ML) on top of MapReduce, where high-level ML scripts with R-like syntax are compiled to programs of MR jobs. The declarative specification of ML algorithms enables—in contrast to existing large-scale machine learning libraries— automatic optimization. SystemML’s primary focus is on data parallelism but many ML algorithms inherently exh...

The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or nance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML eld. This paper describes ...

Training machine learning models at scale is a popular workload for distributed data flow systems. However, as these systems were originally built to fulfill quite different requirements it remains an open question how effectively they actually perform for ML workloads. In this paper we argue that benchmarking of large scale ML systems should consider state of the art, single machine libraries ...

As the field of machine learning (ML) matures, two types of data archives are developing: collections of benchmark data sets used to test the performance of new algorithms, and data stores to which machine learning/data mining algorithms are applied to create scientific or commercial applications. At present, the catalogs of these archives are ad hoc and not tailored to machine learning analysi...

UNLABELLED The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experiment...

This chapter argues that the branch of AI known as Machine Learning (ML) can make useful contributions to music research, if employed in a thoughtful way. After giving a brief introduction to machine learning and discussing some general methodological questions, the article presents an ongoing project by the author as an example of a substantial and highly non-trivial application of machine lea...