Biological Faithfulness is Unnecessary for Machine Learning

The N2pc Is Increased by Perceptual Learning but Is Unnecessary for the Transfer of Learning

BACKGROUND Practice improves human performance in many psychophysical paradigms. This kind of improvement is thought to be the evidence of human brain plasticity. However, the changes that occur in the brain are not fully understood. METHODOLOGY/PRINCIPAL FINDINGS The N2pc component has previously been associated with visuo-spatial attention. In this study, we used event-related potentials (E...

Is regularization unnecessary for boosting?

Recognising Biological Sounds Using Machine Learning

The development of a software system which can detect and identify the ight calls of migrating birds is reported. The system rst produces a spectrogram using a Discrete Fourier transform. Calls are detected in the spectrogram using an ad-hoc combination of local peak-nding and a connectedness measure. Attributes are extracted both globally from the call and from a window moved incrementally thr...

Learning AMP Chain Graphs under Faithfulness

This paper deals with chain graphs under the alternative Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to. We also show that the extension of Meek’s conjecture to AMP chain graphs does not hold, which compromises the development of efficient and correct score+sea...

Learning Marginal AMP Chain Graphs under Faithfulness

Marginal AMP chain graphs are a recently introduced family of models that is based on graphs that may have undirected, directed and bidirected edges. They unify and generalize the AMP and the multivariate regression interpretations of chain graphs. In this paper, we present a constraint based algorithm for learning a marginal AMP chain graph from a probability distribution which is faithful to ...