In text categorization, term weighting methods assign appropriate weights to the terms to improve the classification performance. In this study, we propose an effective term weighting scheme, i.e. tf.rf , and investigate several widely-used unsupervised and supervised term weighting methods on two popular data collections in combination with SVM and kNN algorithms. From our controlled experimen...

Mobile robots operating in unknown urban environments encounter a wide range of complex terrains to which they must adapt their planned trajectory for safe and efficient navigation. Most existing approaches utilize supervised learning classify from either an exteroceptive or proprioceptive sensor modality. However, this requires tremendous amount manual labeling effort each newly encountered te...

Multiple classifier systems have been originally proposed for supervised classification tasks. In the five editions of MCS workshop, most of the papers have dealt with design methods and applications of supervised multiple classifier systems. Recently, the use of multiple classifier systems has been extended to unsupervised classification tasks. Despite its practical relevance, semi-supervised ...

We present a novel method for unsupervised classification, including the discovery of a new category and precise object and part localization. Given a set of unlabelled images, some of which contain an object of an unknown category, with unknown location and unknown size relative to the background, the method automatically identifies the images that contain the objects, localizes them and their...

Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is a central assumption in statistical and machine learning approaches for the classification of unlabelled data. In unsupervised classification this cluster definition underlies a nonparametric approach known as density clustering. In semi-supervised classification, cl...

Breast lesion segmentation in magnetic resonance (MR) images is one of the most important parts of clinical diagnostic tools. Pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow...

soil salinity has been a large problem in arid and semi arid regions. preparation of such maps is useful for natural resource managers. old methods of preparing such maps require a lot of time and cost. multi-spectral remotely sensed dates due to the broad vision and repeating of these imageries is suitable for provide saline soil maps. this investigation is conducted to provide saline soil map...

This paper presents a named entity classification system that utilises both orthographic and contextual information. The random subspace method was employed to generate and refine attribute models. Supervised and unsupervised learning techniques used in the recombination of models to produce the final results.

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corp...

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. This paper extends SUSTAIN so that it can be used to model unsupervised learning data. A modified recruitment mechanism is introduced that creates new conceptual clusters in response to surprising events during learning. Two seemingly contradictory unsupervised learning d...