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415
Ngram Based Text Categorization Method for Improved Data Mining
"... Though naïve Bayes text classifiers are widely used because of its simplicity and effectiveness, the techniques for improving performances of these classifiers have been rarely studied. Naïve Bayes classifiers which are widely used for text classification in machine learning are based on the conditi ..."
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Though naïve Bayes text classifiers are widely used because of its simplicity and effectiveness, the techniques for improving performances of these classifiers have been rarely studied. Naïve Bayes classifiers which are widely used for text classification in machine learning are based
Ngram based test sequence generation from finite state models
 In Proceedings of the 1st Future Internet Workshop (FITTEST
, 2013
"... Abstract. Model based testing offers a powerful mechanism to test applications that change dynamically and continuously, for which only some limited blackbox knowledge is available (this is typically the case of future internet applications). Models can be inferred from observations of real execu ..."
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Cited by 1 (1 self)
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novel test case derivation strategy, based on the computation of the Ngram statistics. Event sequences are generated for which the subsequences of size N respect the distribution of the Ntuples observed in the execution traces. In this way, generated and observed sequences share the same context (up
Ngram based Statistical Makam Detection on Makam Music in Turkey using Symbolic Data”, submitted to ISMIR
, 2012
"... This work studies the effect of different score representations and the potential of ngrams in makam classification for traditional makam music in Turkey. While makams are defined with various characteristics including a distinct set of pitches, pitch hierarchy, melodic direction, typical phrases a ..."
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Cited by 2 (2 self)
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This work studies the effect of different score representations and the potential of ngrams in makam classification for traditional makam music in Turkey. While makams are defined with various characteristics including a distinct set of pitches, pitch hierarchy, melodic direction, typical phrases
Brownian Excursions, Critical Random Graphs and the Multiplicative Coalescent
, 1996
"... Let (B t (s); 0 s ! 1) be reflecting inhomogeneous Brownian motion with drift t \Gamma s at time s, started with B t (0) = 0. Consider the random graph G(n; n \Gamma1 +tn \Gamma4=3 ), whose largest components have size of order n 2=3 . Normalizing by n \Gamma2=3 , the asymptotic joint d ..."
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Cited by 106 (8 self)
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Let (B t (s); 0 s ! 1) be reflecting inhomogeneous Brownian motion with drift t \Gamma s at time s, started with B t (0) = 0. Consider the random graph G(n; n \Gamma1 +tn \Gamma4=3 ), whose largest components have size of order n 2=3 . Normalizing by n \Gamma2=3 , the asymptotic joint
Distributed Representations of Sentences and Documents
 In NAACL HLT
"... Many machine learning algorithms require the input to be represented as a fixedlength feature vector. When it comes to texts, one of the most common fixedlength features is bagofwords. Despite their popularity, bagofwords features have two major weaknesses: they lose the ordering of the words ..."
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Cited by 93 (1 self)
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, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bagofwords models. Empirical results show that Paragraph Vectors outperform bag
Distributional limits for critical random graphs
 In preparation
, 2009
"... We consider the Erdős–Rényi random graph G(n, p) inside the critical window, that is when p = 1/n + λn −4/3, for some fixed λ ∈ R. Then, as a metric space with the graph distance rescaled by n −1/3, the sequence of connected components G(n, p) converges towards a sequence of continuous compact metri ..."
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Cited by 31 (8 self)
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) rescaled by n −1/3 converges in distribution to an absolutely continuous random variable with finite mean. Keywords: Random graphs, GromovHausdorff distance, scaling limits, continuum random tree, diameter. 2000 Mathematics subject classification: 05C80, 60C05.
INCORPORATING FEATURES OF DISTRIBUTION AND PROGRESSION FOR AUTOMATIC MAKAM CLASSIFICATION
"... Automatic classification of makams from symbolic data is a rarely studied topic. In this paper, first a review of an ngram based approach is presented using various representations of the symbolic data. While a high degree of precision can be obtained, confusion happens mainly for makams using (al ..."
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Automatic classification of makams from symbolic data is a rarely studied topic. In this paper, first a review of an ngram based approach is presented using various representations of the symbolic data. While a high degree of precision can be obtained, confusion happens mainly for makams using
Structure Feature Selection For Graph Classification
"... With the development of highly efficient graph data collection technology in many application fields, classification of graph data emerges as an important topic in the data mining and machine learning community. Towards building highly accurate classification models for graph data, here we present a ..."
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Cited by 9 (4 self)
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an efficient graph feature selection method. In our method, we use frequent subgraphs as features for graph classification. Different from existing methods, we consider the spatial distribution of the subgraph features in the graph data and select those ones that have consistent spatial location. We have
Sequence Classification in the JensenShannon Embedding
"... This paper presents a novel approach to the supervised classification of structured objects such as sequences, trees and graphs, when the input instances are characterized by probability distributions. Distances between distributions are computed via the JensenShannon (JS) divergence, which offers ..."
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problems. Two kinds of empirical distributions are considered: (i) the Ngram distributions and (ii) the distributions of the First Passage Times (FPT) between occurrences of substrings. Experimental results on DNA splicing junction detection and protein function prediction illustrate that... Preliminary
Learning TaskDependent Distributed Representations by . . .
, 1995
"... While neural networks are very successfully applied to the processing of fixedlength vectors and variablelength sequences, the current state of the art does not allow the efficient processing of structured objects of arbitrary shape (like logical terms, trees or graphs). We present a connectionist ..."
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Cited by 82 (11 self)
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While neural networks are very successfully applied to the processing of fixedlength vectors and variablelength sequences, the current state of the art does not allow the efficient processing of structured objects of arbitrary shape (like logical terms, trees or graphs). We present a connectionist
Results 1  10
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415