Document clustering or unsupervised document classification has been used to enhance information retrieval. Recently this has become an intense area of research due to its practical importance. Outliers are the elements whose similarity to the centroid of the corresponding category is below some threshold value. In this paper, we show that excluding outliers from the noisy training data signifi...

This paper concerns supervised classification of text. Rocchio, the method we choose for its efficiency and extensibility, is tested on three reference corpora "20NewsGroups", "OHSUMED" and "Reuters", using several similarity measures. Analyzing statistical results, many limitations are identified and discussed. In order to overcome these limitations, this paper presents two main solutions: fir...

We describe two related models to cluster multidimensional time-series under the assumption of an underlying linear Gaussian dynamical process. In the first model, times-series are assigned to the same cluster when they show global similarity in their dynamics, while in the second model times-series are assigned to the same cluster when they show simultaneous similarity. Both models are based o...

PURPOSE The authors are developing a content-based image retrieval (CBIR) CADx system to assist radiologists in characterization of breast masses on ultrasound images. In this study, the authors compared seven similarity measures to be considered for the CBIR system. The similarity between the query and the retrieved masses was evaluated based on radiologists' visual similarity assessments. M...

Motivation: Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting miRNA genes. This technique is based on machine learning, using the Naı̈ve Bayes classifier. It automatically generates a model from the train...

This paper reports on the development of a new hybrid architecture that integrates Learning Classifier Systems (LCS) with Rete-based production systems inference engine to improve the performance of the process of compacting LCS generated rules. While LCS is responsible for generating a complete ruleset from a given breast cancer pathological data-set, an adapted Rete-based inference engine has...

This paper presents a new metric to compute similarities between textual documents, based on the Fisher information kernel as proposed by T. Hofmann. By considering a new point-of-view on the embedding vector space and proposing a more appropriate way of handling the Fisher information matrix, we derive a new form of the kernel that yields significant improvements on an information retrieval ta...

This paper describes our submission to SemEval2014 Task 9: Sentiment Analysis in Twitter. Our model is primarily a lexicon based one, augmented by some preprocessing, including detection of MultiWord Expressions, negation propagation and hashtag expansion and by the use of pairwise semantic similarity at the tweet level. Feature extraction is repeated for sub-strings and contrasting sub-string ...

In many personalised recommendation problems, there are examples of items users prefer or like, but no examples of items they dislike. A state-of-the-art method for such implicit feedback, or one-class collaborative filtering (OC-CF), problems is SLIM, which makes recommendations based on a learned item-item similarity matrix. While SLIM has been shown to perform well on implicit feedback tasks...

In order to solve the problem of image color recognition, this paper proposes a method recognition and optimization based on deep learning designs postprocessing framework word bag model (bow). The uses CNN features calculates feature similarity. sets with high similarity are input into classifier trained by bow clustering as preliminary retrieval results. results categories largest number imag...