Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations....

BACKGROUND The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. METHODS AND FINDING We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2...

This paper reviews the current state of the art of machine learning applied to the Semantic Web. It looks at the Semantic Web and its languages, including RDF and OWL, from a machine learning perspective. Trends in the Semantic Web are mentioned throughout and the relationship with Web Services is examined. Applications are discussed with recent examples and pointers to data sets. Finally, the ...

Grammar induction refers to the process of learning grammars and languages from data; this finds a variety of applications in syntactic pattern recognition, the modeling of natural language acquisition, data mining and machine translation. This special topic contains several papers presenting some of recent developments in the area of grammar induction and language learning, as applied to vario...

Machine learning is a thriving part of computer science. There are many efficient approaches to machine learning that do not provide strong theoretical guarantees, and a beautiful general learning theory. Unfortunately, machine learning approaches that give strong theoretical guarantees have not been efficient enough to be applicable. In this paper we introduce a logical approach to machine lea...

In 2016, Baidu and Google spent somewhere between twenty and thirty billion dollars developing and acquiring artificial intelligence and machine learning technologies (Bughin et al. 2017). A range of other sectors, including health care, education, and manufacturing, are also predicted to adopt these technologies at increasing rates. Machine learning and AI are proven to have the capacity to gr...

Machine learning is a powerful set of techniques that has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, MEG, and PET data. With these new techniques come new dangers of overfitting that are not well understood by the neuroscience community. In this article, we use Support Vector Machine (SVM) classifiers, and genetic algorithms to demonstrate th...

This paper draws connections between Probabilistic Methods and Machine Learning Approaches. In particular, we show that many Support Vector Machine criteria – binary, multi-class, and latent – can be obtained as upper bound approximations to standard probabilistic formulations. The advantage of these ’Machine Learning bounds’ is that it greatly simplifies the computation and, possibly, may yiel...