The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies inte...

Similarity is a crucial issue in Image Retrieval [1–4]. It is relevant both for unsupervised clustering [5, 6] and for supervised classification [7]. In this study, we aim to provide an effective method for learning image similarity. To reach this aim we start of from the Fuzzy Kwan–Cai Neural Network (FKCNN) [8] and turn it into a supervised method for learning similarity. Unlike the classical...

Audio classification has applications in a variety of contexts, such as automatic sound analysis, supervised audio segmentation and in audio information search and retrieval. Extended Baum-Welch (EBW) transformations are most commonly used as a discriminative technique for estimating parameters of Gaussian mixtures, though recently they have been applied in unsupervised audio segmentation. In t...

This project compares the supervised logistic regression segmentation algorithm against the unsupervised k-means clustering segmentation. We observed that the difference between either method is not very significant. When performed on the 100 test cases for BSD300, the supervised method on average achieved a precision rate of 0.47 and the unsupervised method achieved a precision rate of 0.41. T...

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms developed by Simpson. The GFMM method combines the supervised and unsupervised learning within a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering...

Feature selection is a task of fundamental importance for many data mining or machine learning applications, including regression. Surprisingly, most of the existing feature selection algorithms assume the problems to address are either supervised or unsupervised, while supervised and unsupervised samples are often simultaneously available in real-world applications. Semi-supervised feature sel...

Permutation ambiguity of the classical Independent Component Analysis (ICA) may cause problems in feature extraction for pattern classification. Especially when only a small subset of components is derived from data, these components may not be most distinctive for classification, because ICA is an unsupervised method. We include a selective prior for de-mixing coefficients into the classical I...

Automatic classification of text documents has become an important research issue now days. Proper classification of text documents requires information retrieval, machine learning and Natural language processing (NLP) techniques. Our aim is to focus on important approaches to automatic text classification based on machine learning techniques viz. supervised, unsupervised and semi supervised. I...

BACKGROUND Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. METHODS First, unsupervised methods perform a u...