1,720,998 research outputs found
CLASSIFIERS BASED ON A NEW APPROACH TO ESTIMATE THE FISHER SUBSPACE AND THEIR APPLICATIONS
Full text linkIn this thesis we propose a novel classifier, and its extensions, based on a novel estimation of the Fisher Subspace. The proposed classifiers have been developed to deal with high dimensional and highly unbalanced datasets whose cardinality is low. The efficacy of the proposed techniques has been proved by the results achieved on real and synthetic datasets, and by the comparison with state of the art predictors
PIPCAC: A Novel Binary Classifier Assuming Mixtures of Gaussian Functions
No full textProbabilistic classifiers are among the most popular classification methods adopted by the machine learning community. They are often based on a-priori knowledge about the probability distribution underlying the data; nevertheless this information is rarely provided, so that a family of probability distribution functions is assumed to be an approximation model. In this paper we present an efficient binary classifi cation algorithm, called Perceptron-IPCAC (PIPCAC), assuming that each class is distributed accordingly to a Mixture of Gaussian functions. PIPCAC is defined as a multilayer perceptron trained by combining different linear classifiers. The algorithm has been tested on both synthetic and real datasets, and the obtained results demonstrate the effectiveness and efficiency of the proposed method. Furthermore, the promising performances have been confirmed by the comparison of its results with those achieved by Support Vector Machines
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Novel fisher discriminant classifiers
No full textAt the present, several applications need to classify high dimensional points belonging to highly unbalanced classes.Unfortunately, when the training set cardinality is small compared to the data dimensionality (small sample size problem) the classification performance of several well known classifiers strongly decreases.Similarly, the classification accuracy of several discriminative methods decreases when non-linearly separable, and unbalanced, classes are treated.In this paper we firstly survey state of the art methods that employ improved versions of Linear Discriminant Analysis (LDA) to deal with the above mentioned problems; secondly, we propose a family of classifiers based on the Fisher subspace estimation, which efficiently deal with the small sample size problem, nonlinearly separable classes, and unbalanced classes. The promising results obtained by the proposed techniques on benchmark datasets, and the comparison with state of the art predictors, show the efficacy of the proposed techniques
Impact of security on speech quality
Full text linkThis paper deals with impact of secured environment on speech quality of IP telephony. There are presented the results of the analyzing of voice over secure communication links based on TLS. The using of secure network environments can affect a speech quality. There is the performance comparision of cipher alghorithms and description how the used security mechanisms influence the final R- factor. The presented results are based on numerous of experiments which have been performed in real IP networ
Peripheral sequelae of brain focal ischaemia on male reproductive function: role of purinergic and adrenergic transmission.
No full textO-IPCAC and its application to EEG classification
No full textIn this paper we describe an online/incremental linear binary classifier based on an inter- esting approach to estimate the Fisher subspace. The proposed method allows to deal with datasets having high cardinality, being dynamically supplied, and it efficiently copes with
high dimensional data without employing any dimensionality reduction technique. More-
over, this approach obtains promising classification performance even when the cardinality of the training set is comparable to the data dimensionality.
We demonstrate the efficacy of our algorithm by testing it on EEG data. This classifi-
cation problem is particularly hard since the data are high dimensional, the cardinality of the data is lower than the space dimensionality, and the classes are strongly unbalanced.
The promising results obtained in the MLSP competition, without employing any feature
extraction/selection step, have demonstrated that our method is effective; this is further
proved both by our tests and by the comparison with other well-known classifiers
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Novel high intrinsic dimensionality estimators
No full textRecently, a great deal of research work has been devoted to the development of algorithms to estimate the intrinsic dimensionality (id) of a given dataset, that is the minimum number of parameters needed to represent the data without information loss. id estimation is important for the following reasons: the capacity and the generalization capability of discriminant methods depend on it; id is a necessary information for any dimensionality reduction technique; in neural network design the number of hidden units in the encoding middle layer should be chosen according to the id of data; the id value is strongly related to the model order in a time series, that is crucial to obtain reliable time series predictions.Although many estimation techniques have been proposed in the literature, most of them fail on noisy data, or compute underestimated values when the id is sufficiently high. In this paper, after reviewing some of the most important id estimators related to our work, we provide a theoretical motivation of the bias that causes the underestimation effect, and we present two id estimators based on the statistical properties of manifold neighborhoods, which have been developed in order to reduce this effect. We exhaustively evaluate the proposed techniques on synthetic and real datasets, by employing an objective evaluation measure to compare their performance with those achieved by state of the art algorithms; the results show that the proposed methods are promising, and produce reliable estimates also in the difficult case of datasets drawn from non-linearly embedded manifolds, characterized by high id
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