The distinction between supervised and unsupervised categorization has had a profound influence in related research. We consider a mechanism, attentional selection, possibly shared by models of supervised and unsupervised categorization. In supervised models such as the Generalized Context Model (GCM; Nosofsky, 1988), attentional selection emphasizes dimensions which are relevant for a taught c...

Classification techniques deploy supervised labeled instances to train classifiers for various classification problems. However labeled instances are limited, expensive, and time consuming to obtain, due to the need of experienced human annotators. Meanwhile large amount of unlabeled data is usually easy to obtain. Semi-supervised learning addresses the problem of utilizing unlabeled data along...

In this paper we present a study for semi-supervised multiclass web page classification using SVM. We propose not only combining binary semi-supervised classifiers, but also multiclass supervised ones. Our experiments show great performance for the latter method, where ignoring unlabeled documents could be better for some cases, using only labeled documents for the learning task, directly based...

Most of the classifiers suffer from the curse of dimensionality during classification of high dimensional image and non-image data. In this paper, we introduce a new supervised nonlinear dimensionality reduction (S-NLDR) algorithm called supervised dimensionality reduction based on evolution strategy (SDRES) for both image and nonimage data. The SDRES method uses the power of evolution strategy...

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the in...

Network Traffic Classification is an important process in various network management activities like network planning, designing, workload characterization etc. Network traffic classification using traditional techniques such as well known port number based and payload analysis based techniques are no more effective because various applications uses port hopping and encryption technique to avoi...

Gene expression data always suffer from the high dimensionality issue, therefore feature selection becomes a fundamental tool in the analysis of cancer classification. Basically, the data can be collected easily without providing the label information, which is quite useful in improving the accuracy of the classification. Label information usually difficult to obtain as the labelling processes ...

In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectationmaximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the appro...

We present new nearest neighbor methods for text classification and an evaluation of these methods against the existing nearest neighbor methods as well as other well-known text classification algorithms. Inspired by the language modeling approach to information retrieval, we show improvements in k-nearest neighbor (kNN) classification by replacing the classical cosine similarity with a KL dive...

Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. ( 2011 ; Pothos et al., 2008 ) with the results from two procedures of supervised categorization. In...