Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data

Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data

Marchand and Shawe-Taylor (2002) have proposed a loss bound for the set covering machine that has the property to depend on the observed fraction of positive examples and on what the classifier achieves on the positive training examples. We show that this loss bound is incorrect. We then propose a loss bound, valid for any sample-compression learning algorithm (including the set covering machin...

Discrete Polynomial Curve Fitting to Noisy Data

A discrete polynomial curve is defined as a set of points lying between two polynomial curves. This paper deals with the problem of fitting a discrete polynomial curve to given integer points in the presence of outliers. We formulate the problem as a discrete optimization problem in which the number of points included in the discrete polynomial curve, i.e., the number of inliers, is maximized. ...

Delving into BioMEMS.

Delving into Transition to the Semantic Web

The semantic technologies pose new challenge for the way in which we built and operate systems. They are tools used to represent significances, associations, theories, separated from data and code. Their goal is to create, to discover, to represent, to organize, to process, to manage, to ratiocinate, to represent, to share and use the significances and knowledge to fulfill the business, persona...

Feature Curve Co-Completion in Noisy Data

Feature curves on 3D shapes provide important hints about significant parts of the geometry and reveal their underlying structure. However, when we process real world data, automatically detected feature curves are affected by measurement uncertainty, missing data, and sampling resolution, leading to noisy, fragmented, and incomplete feature curve networks. These artifacts make further processi...