Efficient Non-Parametric Function Induction in Semi-Supervised Learning

In the following set of experiments with the practical FOM and CG algorithms, we assume that the products Ap can be computed in double, single or half precision (with

De la même manière qu’un comportement donné peut être envisagé sous l’angle exclusif du rapport à soi ou également comme ayant un impact sur autrui, un

With a set of 1, 000 labeling, the SVM classification error is 4.6% with SVM training set of 60 labels (1% of the database size), with no kernel optimization nor advanced

Contrary to the lack of clearly supported species clusters in the sequence analyses, the AFLP markers resolved the phylogeny with higher bootstrap and clade credibility support.

A typical scenario for apprenticeship learning is to have an expert performing a few optimal (or close to the optimal) trajectories and use these to learn a good policy.. In this

In this article, a new approach is proposed to study the per- formance of graph-based semi-supervised learning methods, under the assumptions that the dimension of data p and

Mixmod is a well-established software package for fitting a mixture model of mul- tivariate Gaussian or multinomial probability distribution functions to a given data set with either