Kernel Credal C-Means Method Based on Belief Functions

Credal sets approximation by belief functions

Local Self-concordance of Barrier Functions Based on Kernel-functions

 Many efficient interior-point methods (IPMs) are based on the use of a self-concordant barrier function for the domain of the problem that has to be solved. Recently, a wide class of new barrier functions has been introduced in which the functions are not self-concordant, but despite this fact give rise to efficient IPMs. Here, we introduce the notion of locally self-concordant barrier functio...

Kernel Penalized K-means: A feature selection method based on Kernel K-means

Article history: Received 11 June 2014 Received in revised form 23 October 2014 Accepted 11 June 2015 Available online 19 June 2015

An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal Semantics

Evidence theory extends Bayesian probability theory by allowing for a more expressive model of subjective uncertainty. Besides standard interpretation of belief functions, where uncertainty corresponds to probability masses which might refer to whole subsets of the possibility space, credal semantics can be also considered. Accordingly, a belief function can be identified with the whole set of ...

Semi-supervised Kernel-Based Fuzzy C-Means

This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S2KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algorithm. Through using labeled and unlabeled data together, S2KFCM can be applied to both clustering and classification tasks. However, only the latter is concerned in this paper. Expe...