Weighted inductive means

Bilateral Weighted Fuzzy C-Means Clustering

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...

Constructing Orders By Means of Inductive Definitions

Importance Weighted Inductive Transfer Learning for Regression

We consider inductive transfer learning for dataset shift, a situation in which the distributions of two sampled, but closely related, datasets differ. When the target data to be predicted is scarce, one would like to improve its prediction by employing data from the other, secondary, dataset. Transfer learning tries to address this task by suitably compensating such a dataset shift. In this wo...

Weighted Power Means of Operators

Let σ1,σ2, · · · ,σn be positive real numbers satisfying n ∑ i=1 σi = 1 and A1,A2, · · · ,An be positive operators. Let Mσ,γ(A) = ( n ∑ i=1 σiA γ i ) 1/γ, γ > 0. It has been shown that lim γ→0+ Mσ,γ exists. Some known inequalities have also been generalized.

On Weighted Randomly Trimmed Means

A class of robust location estimators called weighted randomly trimmed means are introduced and not only their consistency and asymptotic normality are proved, but their influence functions, asymptotic variances and breakdown points are also derived. They possess the same breakdown points as the median, and some of them own higher asymptotic relative efficiencies at the heavy-tailed distributio...