Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for...

Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy pattern recognition. It has a number of advantages over other pattern recognition methods, including simpler methods of attribute selection or ability to learn in real time environments. These methods build a prototype for each attribute and combine the partial estimations of each prototype by a fusion ope...

Fuzzy Classification is of great interest because of its capacity to provide more useful information for Geographic Information Systems. This paper describes an Explicit Fuzzy Supervised Classification method, which consists of three steps. The explicit fuzzyfication is the first step where the pixel is transformed into a matrix of membership degrees representing the fuzzy inputs of the process...

In machine learning, whether one can build a more accurate classifier by using unlabeled data (semi-supervised learning) is an important issue. Although a number of semi-supervised methods have been proposed, their effectiveness on NLP tasks is not always clear. This paper presents a novel semi-supervised method that employs a learning paradigm which we call structural learning. The idea is to ...

Aiming at the deficiency of supervise information in the process of sparse reconstruction in Sparsity Preserving Projections (SPP), a semi-supervised dimensionality reduction method named Constraint-based Sparsity Preserving Projections (CSPP) is proposed. CSPP attempts to make use of supervision information of must-link constraints and cannot-link constraints to adjust the sparse reconstructiv...

Horizontal collaborative clustering is such a clustering method that carries clustering on one data set describing a pattern set in one feature space with collaborative introducing of outer partition information obtained by clustering on another data set but describing the same pattern set in another feature space. In order to implement the collaborative clustering, horizontal collaborative fuz...

In this paper, we present the methods involved in the tasks 4 and 5 of the TECHNOVISION/ImagEval contest. Two techniques are used, a global and a local one, both based on a description of low level visual content by histograms of colors and textures. The global approach considers images as a whole, and classifies images using a single histogram for each image. Kernel functions and SVMs are used...

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-ty...

In order to analyze the logical foundation of fuzzy reasoning, this paper first introduces the concept of generalized roots of theories in Łukasiewicz propositional fuzzy logic Łuk, Gödel propositional fuzzy logic Göd, Product propositional fuzzy logic P, and nilpotent minimum logic NM (the R0-propositional fuzzy logic L ). Next, it is proved that all consequences of a theory C, named D(C), are...