Search results for "RNR"
showing 10 items of 302 documents
A remark on hyperplane sections of rational normal scrolls
2017
We present algebraic and geometric arguments that give a complete classification of the rational normal scrolls that are hyperplane section of a given rational normal scrolls.
Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models
2017
Topic models for text analysis are most commonly trained using either Gibbs sampling or variational Bayes. Recently, hybrid variational-Gibbs algorithms have been found to combine the best of both worlds. Variational algorithms are fast to converge and more efficient for inference on new documents. Gibbs sampling enables sparse updates since each token is only associated with one topic instead of a distribution over all topics. Additionally, Gibbs sampling is unbiased. Although Gibbs sampling takes longer to converge, it is guaranteed to arrive at the true posterior after infinitely many iterations. By combining the two methods it is possible to reduce the bias of variational methods while …
A Survey of Multi-Label Topic Models
2019
Every day, an enormous amount of text data is produced. Sources of text data include news, social media, emails, text messages, medical reports, scientific publications and fiction. To keep track of this data, there are categories, key words, tags or labels that are assigned to each text. Automatically predicting such labels is the task of multi-label text classification. Often however, we are interested in more than just the pure classification: rather, we would like to understand which parts of a text belong to the label, which words are important for the label or which labels occur together. Because of this, topic models may be used for multi-label classification as an interpretable mode…
Computer-Aided Diagnosis System with Backpropagation Artificial Neural Network—Improving Human Readers Performance
2016
This article presents the results of a study into possibility of artificial neural networks (ANNs) to classify cancer changes in mammographic images. Today’s Computer-Aided Detection (CAD) systems cannot detect 100 % of pathological changes. One of the properties of an ANN is generalized information —it can identify not only learned data but also data that is similar to training set. The combination of CAD and ANN could give better result and help radiologists to take the right decision.
Feature Selection for Ensembles of Simple Bayesian Classifiers
2002
A popular method for creating an accurate classifier from a set of training data is to train several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. However, the simple Bayesian classifier has much broader applicability than previously thought. Besides its high classification accuracy, it also has advantages in terms of simplicity, learning speed, classification speed, storage space, and incrementality. One way to generate an ensemble of simple Bayesian classifiers is to use different feature subsets as in the random subspace method. In this paper we present a technique for building ensembles o…
Ensemble Feature Selection Based on the Contextual Merit
2001
Recent research has proved the benefits of using ensembles of classifiers for classification problems. Ensembles constructed by machine learning methods manipulating the training set are used to create diverse sets of accurate classifiers. Different feature selection techniques based on applying different heuristics for generating base classifiers can be adjusted to specific domain characteristics. In this paper we consider and experiment with the contextual feature merit measure as a feature selection heuristic. We use the diversity of an ensemble as evaluation function in our new algorithm with a refinement cycle. We have evaluated our algorithm on seven data sets from UCI. The experiment…
Ensemble Feature Selection Based on Contextual Merit and Correlation Heuristics
2001
Recent research has proven the benefits of using ensembles of classifiers for classification problems. Ensembles of diverse and accurate base classifiers are constructed by machine learning methods manipulating the training sets. One way to manipulate the training set is to use feature selection heuristics generating the base classifiers. In this paper we examine two of them: correlation-based and contextual merit -based heuristics. Both rely on quite similar assumptions concerning heterogeneous classification problems. Experiments are considered on several data sets from UCI Repository. We construct fixed number of base classifiers over selected feature subsets and refine the ensemble iter…
Bot recognition in a Web store: An approach based on unsupervised learning
2020
Abstract Web traffic on e-business sites is increasingly dominated by artificial agents (Web bots) which pose a threat to the website security, privacy, and performance. To develop efficient bot detection methods and discover reliable e-customer behavioural patterns, the accurate separation of traffic generated by legitimate users and Web bots is necessary. This paper proposes a machine learning solution to the problem of bot and human session classification, with a specific application to e-commerce. The approach studied in this work explores the use of unsupervised learning (k-means and Graded Possibilistic c-Means), followed by supervised labelling of clusters, a generative learning stra…
Methodology for the estimation of the increase in time loss due to future increase in tropical cyclone intensity in Japan
2009
Published version of an article from the journal: Climatic Change. The original publication is available at Spingerlink. http://dx.doi.org/10.1007/s10584-009-9725-9 The present paper develops a methodology for estimating the risks and consequences of possible future increases in tropical cyclone intensities that would allow policy makers to relatively quickly evaluate the cost of different mitigation strategies. The methodology simulates future tropical cyclones by modifying the intensity of historical tropical cyclones between the years 1978 and 2007. It then uses a Monte Carlo Simulation to obtain the expected number of hours that a certain area can expect to be affected by winds of a giv…
Evaluación multicriterio de políticas de uso y gestión de recursos naturales
2014
The purpose of this work is show the characteristics of different environmental economic assessment methodologies, and exposes a case study where possible alternatives addressed to reduce the contamination of a bay with multicriteria approach are evaluated. The results obtained show the effectiveness of multicriteria analysis to evaluate the alternatives for solution of contamination problem in a bay. Economic evaluation of the contamination effects integrated to the environmental, social and economic dimensions through multicriteria techniques allows an alternative planning to solve the environmental problem studied, providing the right decision.