Search results for "RECOGNITION"
showing 10 items of 3607 documents
Study time effects in recognition memory.
2004
We empirically tested the assumption that study time increases recognition accuracy because the storage of information is better when study time is longer as Shiffrin and colleagues have reported, an assumption common to parallel models of recognition. In the present study with 123 subjects, we examined the effect of item strength on four measures: hit rate, false alarm rate, d′, and β, for a single-word recognition task with longer study times than those usually used in the literature. Analysis indicated significant increase for hit rate and d′ and a decrease in false alarm rate, as one goes from weak to stronger study conditions, and a change in ln(β) when study time is greater than 1 se…
Determination of Synchronization of Electrical Activity in the Heart by Shannon Entropy Measure
2005
In this paper we propose a new index of synchronization for the study of heart’s electrical activity during atrial fibrillation (AF). The index relies on the measure of the time delays between correspondent activations in two atrial electrograms and on the characterization of their dispersion by a measure of Shannon Entropy. The algorithm was validated on simulated signals mimicking different degree of synchronization. Results showed the index was able to discriminate among different levels of organization, provided that it works on series of at least 50 activations (time resolution of almost 10 sec during AF). Moreover, we applied the algorithm to real bipolar electrograms, obtained from a…
Color memory in protanomals and deuteranomals: Matching time effect
2003
In a companion paper Perez-Carpinell et al., Color Res Appl 2001;26:158–170, for a set of seven color references, we compared the methods of simultaneous and memory color matching by 15 protanomals and 21 deuteranomals, looking for differences between them and a group of 25 normal trichromat observers investigated previously Perez-Carpinell et al., Color Res Appl 1998;23:234–247. In our current article, matching times of the same anomalous trichromat groups, and with the same reference tests, to select from among the comparison chips the one that most resembled one of the seven reference tests, have been measured under simultaneous and successive color matching procedures. From comparison b…
Multi-label Classification Using Stacked Hierarchical Dirichlet Processes with Reduced Sampling Complexity
2018
Nonparametric topic models based on hierarchical Dirichlet processes (HDPs) allow for the number of topics to be automatically discovered from the data. The computational complexity of standard Gibbs sampling techniques for model training is linear in the number of topics. Recently, it was reduced to be linear in the number of topics per word using a technique called alias sampling combined with Metropolis Hastings (MH) sampling. We propose a different proposal distribution for the MH step based on the observation that distributions on the upper hierarchy level change slower than the document-specific distributions at the lower level. This reduces the sampling complexity, making it linear i…
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…
A genetic algorithm for combined topology and shape optimisations
2003
A method to find optimal topology and shape of structures is presented. With the first the optimal distribution of an assigned mass is found using an approach based on homogenisation theory, that seeks in which elements of a meshed domain it is present mass; with the second the discontinuous boundaries are smoothed. The problem of the optimal topology search has an ON/OFF nature and has suggested the employment of genetic algorithms. Thus in this paper a genetic algorithm has been developed, which uses as design variables, in the topology optimisation, the relative densities (with respect to effective material density) 0 or 1 of each element of the structure and, in the shape one, the coord…
Sample–tip coupling efficiencies of the photon-scanning tunneling microscope
1991
The photon-scanning tunneling microscope is the photon analog to the electron-scanning tunneling microscope. It uses the evanescent field due to the total internal reflection of a light beam in a prism, modulated by a sample attached to the prism. The exponential decay of the evanescent field is characterized by the penetration depth dp and depends on the angle of incidence θ, the wavelength, and the polarization of the incident beam. The 1/e decay lengths range from 150 to 265 nm as deduced from the expression of the electric-field intensity in the rarer medium for θ = π/2. If we place another optically transparent medium near the surface, frustrated total reflection occurs. It is shown th…
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…