Mixture model estimation with soft labels

In this paper, we propose a super- vised learning algorithm based on a Gaussian Mixture model which not only generalizes the K-means algorithm by allowing soft assignments, but also

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

The SAEM algorithm is guaranteed to converge to a local maximum of the likelihood function for models in the exponential family, under general conditions that include the

In this paper we propose an alternative solution to the above problem of estimating a general conditional density function of tar- gets given inputs, in the form of a Gaussian

Many popular clustering algorithms have been ex- tended to partial supervision including Fuzzy C-Means in [11], [4], Hierarchical Clustering Algorithms in [1], Point-

To evaluate the WCET of the basic block, we describe the execution of an instruction path on a processor as a 2 This definition makes sense when instructions terminate in program

The results were obtained using soft labels with discounting (-.+), supervised learning (-∗), unsupervised learning (- -o), adaptive semi-supervised learning (..•) and the