Abstract. Occluding contour (OC) plays important roles in many computer vision tasks. The study of using OC for visual inference tasks is however limited, partially due to the lack...
Jin Sun, Christopher Thorpe, Nianhua Xie, Jingyi Y...
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...
Exponential models of distributions are widely used in machine learning for classification and modelling. It is well known that they can be interpreted as maximum entropy models u...
Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why K-Neares...
Data mining has emerged to be a very important research area that helps organizations make good use of the tremendous amount of data they have. In data classification tasks, fuzzy ...
In this paper, we address the question of what kind of knowledge is generally transferable from unlabeled text. We suggest and analyze the semantic correlation of words as a gener...
In some classification tasks, all patterns in a field, such as digits in a ZIP-code image, originate from the same, but unknown, source (writer/print style). The class-conditional...
The aim of this paper is to show how abduction can be used in classification tasks when we deal with incomplete data. Some classifiers, even if based on decision tree induction lik...
We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of proble...
Justin Martineau, Tim Finin, Anupam Joshi, Shamit ...
Most classification tasks that have been tackled for automation are ones involving complex chains of causal reasoning. However, our daily lives are filled with simple classificati...