[PDF] Top 20 Software Fault Proneness Prediction Using Support Vector Machines
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Software Fault Proneness Prediction Using Support Vector Machines
... of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software ...a Support Vector Machine (SVM) model to find ... See full document
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Automatic Prediction of Cognate Orthography Using Support Vector Machines
... cognate prediction: given a sequence of symbols – ...on Support Vector Machines developed by Gimenez and Marquez ...similar software showed that SVMTool was the most suitable one for ... See full document
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Fault Classification of Reciprocating Compressor Based on Neural Networks and Support Vector Machines
... the software designer to allow for sparseness of data and which can lead to a better performance for SVMs than ANNs ...for fault diagnosis may be larger than could be used for ... See full document
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Software Vulnerability Prediction Using Feature Subset Selection and Support Vector Machine
... the software based on the failures that have surfaced during ...industrial software system and will not necessarily yield the same results on different software ... See full document
7
Software defect prediction using enhanced relevance vector machine
... learning, support vector machines (SVMs) produce a model function dependent only on a subset of kernel basis functions associated with some of the training samples, ...of support vectors is ... See full document
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Application of Support Vector Machines to Fault Diagnosis and Automated Repair
... of fault diagnosis and automated re- ...port Vector machines, are well suited to this ...each prediction given by the algorithm, as this would greatly enhance the usability of a decision ... See full document
5
Thermal Image Based Fault Diagnosis of Gears using Support Vector Machines
... and fault diagnosis of working machines have gained significant attention due to their prospective benefits, such as enhanced productivity, decreased repair and maintenance costs and enhanced machine ... See full document
6
Prediction of soil physical properties by optimized support vector machines
... defined using geology, topography, and land use maps in the environment of ILWIS ...3.4 software (ITC, University of Twente, Netherlands) to collect soil ...med using the procedure described by Gee ... See full document
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Predicting Software Fault Proneness Using Machine Learning
... defect prediction capability by computing the accuracy of their proposed model on di ff erent datasets obtained from di ff erent repositories for a given ...sources, using 14 di ff erent classifiers for ... See full document
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Risk chain prediction metrics for predicting fault proneness in Software Systems
... LSI is a machine-learning model that induces representations of the meaning of words by analyzing the relations among words and documents in textual corpus of data. LSI was initially developed in the context of ... See full document
6
One-class support vector machines for protein-protein interactions prediction
... by using one of the available databases of interacting proteins, there is no data on experimentally confirmed non-interacting protein pairs have been made ... See full document
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Support Vector Machines for Prediction of Futures Prices in Indian Stock Market
... We have applied Vapnik‟s SVM for regression by using LS- SVM tool box. The typical kernel functions used in SVRs are the Polynomial Kernel k (x, y) = (x * y + 1) d and the Gaussian Kernel k (x, y) = exp (-(|x – ... See full document
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Smart Agriculture Monitoring Using Environmental Data Analysis of Support Vector Machines with Weather Prediction
... The notion in this paper is that we can avoid duplicate copies of storage data and limit the damage of stolen data by decreasing the value of that stolen information to the attacker. This paper comples the first attempt ... See full document
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Optimized Support Vector Machine for Software Defect Prediction
... SVM classifier is trained before use; thus reduced input data is partitioned (yi), i=l,…,n into 2, T ⊂{l,…,n} training set and V ⊂{l,…,n} testing (or validation) set with T ∪ V = {l,…,n} and T ∩V={}. Training data set T ... See full document
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Epileptic Seizure Classification of EEG Image Using SVM
... epilepsy. Using the test images the accuracy of the SVM classifier is ...images using the features extracted from DWT has higher classification accuracy than the SVM classification that uses the features ... See full document
5
Sparse Deconvolution Using Support Vector Machines
... applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution ... See full document
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Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
... involving only the columns of G corresponding to the indices for which (α (k+1) i − α (k) i ) 6 = 0, i ∈ B . Further improvements in the number of kernel evaluations can be obtained by introducing a caching strategy, ... See full document
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Probabilistic Sentence Reduction Using Support Vector Machines
... of that configuration. Algorithm 1 shows a probabilistic sentence reduction using the top K-BFS search algorithm. This algorithm uses a breadth-first search which does not expand the entire frontier, but instead ... See full document
7
A Neuro Based Software Fault Prediction with Box Cox Power Transformation
... neuro-based software fault ...ware fault counts as well as make the long-term ...long-term prediction of software faults with the grouped data in the ANN ...other software ... 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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