Feature selection is fundamental in many data mining or machine learning applications. Most of the algorithms proposed for this task make the assumption that the data are either supervised or unsupervised, while in practice supervised and unsupervised samples are often simultaneously available. Semi-supervised feature selection is thus needed, and has been studied quite intensively these past f...

Statistical methods for PP attachment fall into two classes according to the training material used: first, unsupervised methods trained on raw text corpora and second, supervised methods trained on manually disambiguated examples. Usually supervised methods win over unsupervised methods with regard to attachment accuracy. But what if only small sets of manually disambiguated material are avail...

This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning a...

This dissertation presents several new methods of supervised and unsupervised learning of word sense disambiguation models. The supervised methods focus on performing model searches through a space of probabilistic models, and the unsupervised methods rely on the use of Gibbs Sampling and the Expectation Maximization (EM) algorithm. In both the supervised and unsupervised case, the Naive Bayesi...

PURPOSE To compare the complication rates associated with orthopaedic trauma surgery performed by unsupervised and supervised trainees. METHODS In our hospital, 6361 orthopaedic trauma operations were performed between 1 January 1998 and 31 December 2002. Data pertinent to the surgeon's supervision and postoperative complications were collected. Elective operations were excluded, as consultan...

We apply an unsupervised machine learning approach for Internet traffic identification and compare the results with that of a previously applied supervised machine learning approach. Our unsupervised approach uses an Expectation Maximization (EM) based clustering algorithm and the supervised approach uses the Naı̈ve Bayes classifier. We find the unsupervised clustering technique has an accuracy ...

Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised unsupervised learning methods create models for predicting an machine. main objective was determine when asset either in its nominal operation or working outside this zone, thus being at risk failure sub-optimal operation. results showed that it possible classi...

In this paper, we study unsupervised image segmentation using wavelet-domain hidden Markov models (HMMs). We first review recent supervised Bayesian image segmentation algorithms using wavelet-domain HMMs. Then, a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs. The K-mean clustering is u...