[PDF] Top 20 Probabilistic Sentence Reduction Using Support Vector Machines
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Probabilistic Sentence Reduction Using Support Vector Machines
... based sentence reduction method using machine learning ...of sentence re- duction using available ...given sentence and its reduced sentence are the ...new sentence ... See full document
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Sparseness of Support Vector Machines
... Downs et al. (2001) proposed a technique which finds samples that are linearly dependent in the RKHS in order to construct representations that are more sparse than the ones found by optimizing the dual of the L1-SVM ... See full document
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Comparison of Neural Networks and Support Vector Machines using PCA and ICA for Feature Reduction
... The performance of machine learning process is dependent on its features. The fundamental challenge in machine learning is how to extract the features that best represent the original content. Integration of Principal ... See full document
6
False Positives Reduction in Top-down Protein Informatics using Support Vector Machines
... weight using a mass spectrometer [5], where it can also be fragmented as a result of destabilization caused by shots of high speed electrons onto its molecular ... See full document
5
High Performance Implementation of Support Vector Machines Using OpenCL
... binary reduction (sec- ond phase) portion of our two phase reduction, where data is copied between two threads on every iteration; however, we use a thread block size which crosses warp boundaries and we ... See full document
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Shallow Semantic Parsing using Support Vector Machines
... a sentence, a parser should, for each predicate in the sentence, identify and label the predicate’s seman- tic ...a sentence that represent these semantic argu- ments and assigning specific labels to ... See full document
8
Detecting Errors in Corpora Using Support Vector Machines
... conventional probabilistic ap- proaches for corpus error detection, although precise comparison is difficult, our approach achieved relatively high ...precision. Using a prob- abilistic approach, Murata et ... See full document
7
Ranking and Searching of Document with New Innovative Method in Text Mining: First Review
... retrivel) probabilistic model, rocchio model [ ] [ ] BM25 and SVM (support Vector machines) which facilitate filtering of relevant data ... See full document
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Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction
... In this paper, we proposed a novel data reduction method SIFS to simultaneously identify inactive features and samples for sparse SVMs. Our major contribution is a novel framework for an accurate estimation of the ... See full document
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Artificial Intelligence Based Fault Diagnosis of Power Transformer-A Probabilistic Neural Network and Interval Type-2 Support Vector Machine Approach
... both probabilistic neural network (PNN) and Interval Type-2 Fuzzy Support Vector Machine ...as probabilistic neural network ...classified using AI ...KEYWORDS: Probabilistic ... See full document
12
Multibiometric identification by using ear, face, and thermal face
... tree, support vector machines, and probabilistic neural network) are trained by using two fusion methods which are matching score level and feature level ... See full document
8
Dimension Reduction in Text Classification with Support Vector Machines
... in vector space based methods for text classification (20; 21), including the high dimensionality of the input space, sparsity of document vectors, linear separability in most text classification problems, and the ... See full document
17
Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section)
... Variable selection methods are often divided along two lines: filter and wrapper methods (Ko- havi and John, 1997). The filter approach of selecting variables serves as a preprocessing step to the induction. The main ... See full document
15
Automatic Prediction of Cognate Orthography Using Support Vector Machines
... from sentence level to word level, that is to produce a tag for single letters instead of whole ...on Support Vector Machines developed by Gimenez and Marquez ... See full document
6
Counting People in Simultaneous Speech using Support Vector Machines
... The purpose of this paper is to look at the feasibility of counting the number of simultaneous speakers from an au- dio clip. Currently, there are multiple ways to automat- ically count people. WiFi, Bluetooth, and video ... See full document
6
Covering Numbers and Support Vector Machines
... In the traditional viewpoint of statistical learning theory, one is given a class of functions , and the generalization perfor- mance attainable using is determined via the covering num- bers (precise definitions ... See full document
12
Support Vector Machines for Face Recognition
... system. Using something we know and hold are two easy identification/verification solutions widely used ...today. Using something we know only requires a good memory, but sometimes can easily be ... See full document
13
Speaker verification using sequence discriminant support vector machines
... Each component of the score-space corresponds to the derivative of the log-likelihood score with respect to one of the parameters of the model. In some ways, it is a measure of how well the sequence matches the model. ... See full document
9
Using Least Squares Support Vector Machines for Frequency Estimation
... A Support Vector Machine (SVM) [8] uses training data as an integral element of the function estimation model as opposed to simply using training data to esti- mate parameters of an a priori model ... See full document
5
Robustness and Regularization of Support Vector Machines
... Support Vector Machines (SVMs for short) originated in Boser et al. (1992) and can be traced back to as early as Vapnik and Lerner (1963) and Vapnik and Chervonenkis (1974). They continue to be one ... See full document
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