Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism

2015 ◽  
Vol 53 (7) ◽  
pp. 609-622 ◽  
Author(s):  
Surendra Kumar ◽  
Subhojit Ghosh ◽  
Suhash Tetarway ◽  
Rakesh Kumar Sinha
Keyword(s):  
Support Vector Machine ◽  
Motor Cortex ◽  
Comparative Evaluation ◽  
Chronic Alcoholism ◽  
Support Vector

Adaptive Hybrid Semantic Selection of SAWSDL Services with SAWSDL-MX2

2010 ◽  
Vol 6 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Matthias Klusch ◽  
Patrick Kapahnke ◽  
Ingo Zinnikus
Keyword(s):  
Support Vector Machine ◽  
Comparative Evaluation ◽  
Structural Similarity ◽  
Support Vector ◽  
Test Collection ◽  
Semantic Matching ◽  
Retrieval Performance ◽  
Text Similarity ◽  
Random Subset ◽  
Selection Of

In this paper, the authors present an adaptive, hybrid semantic matchmaker for SAWSDL services, called SAWSDL-MX2. It determines three types of semantic matching of an advertised service with a requested one, which are described in standard SAWSDL: logic-based, text-similarity-based and XML-tree edit-based structural similarity. Before selection, SAWSDL-MX2 learns the optimal aggregation of these different matching degrees off-line over a random subset of a given SAWSDL service retrieval test collection by exploiting a binary support vector machine-based classifier with ranking. The authors present a comparative evaluation of the retrieval performance of SAWSDL-MX2.

scholarly journals An Evaluation Model and Benchmark for Parallel Computing Frameworks

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Weibei Fan ◽  
Zhijie Han ◽  
Ruchuan Wang
Keyword(s):  
Support Vector Machine ◽  
Parallel Computing ◽  
Comparative Evaluation ◽  
Large Scale ◽  
Evaluation Model ◽  
Performance Model ◽  
Support Vector ◽  
Large Scale Data ◽  
Performance Evaluation Model ◽  
Scale Data

MARS and Spark are two popular parallel computing frameworks and widely used for large-scale data analysis. In this paper, we first propose a performance evaluation model based on support vector machine (SVM), which is used to analyze the performance of parallel computing frameworks. Furthermore, we give representative results of a set of analysis with the proposed analytical performance model and then perform a comparative evaluation of MARS and Spark by using representative workloads and considering factors, such as performance and scalability. The experiments show that our evaluation model has higher accuracy than multifactor line regression (MLR) in predicting execution time, and it also provides a resource consumption requirement. Finally, we study benchmark experiments between MARS and Spark. MARS has better performance than Spark in both throughput and speedup in the executions of logistic regression and Bayesian classification because MARS has a large number of GPU threads that can handle higher parallelism. It also shows that Spark has lower latency than MARS in the execution of the four benchmarks.

Adaptive Hybrid Semantic Selection of SAWSDL Services with SAWSDL-MX2

2012 ◽  
pp. 146-171 ◽  
Author(s):  
Matthias Klusch ◽  
Patrick Kapahnke ◽  
Ingo Zinnikus
Keyword(s):  
Support Vector Machine ◽  
Comparative Evaluation ◽  
Structural Similarity ◽  
Support Vector ◽  
Test Collection ◽  
Semantic Matching ◽  
Retrieval Performance ◽  
Text Similarity ◽  
Random Subset ◽  
Selection Of

In this paper, the authors present an adaptive, hybrid semantic matchmaker for SAWSDL services, called SAWSDL-MX2. It determines three types of semantic matching of an advertised service with a requested one, which are described in standard SAWSDL: logic-based, text-similarity-based and XML-tree edit-based structural similarity. Before selection, SAWSDL-MX2 learns the optimal aggregation of these different matching degrees off-line over a random subset of a given SAWSDL service retrieval test collection by exploiting a binary support vector machine-based classifier with ranking. The authors present a comparative evaluation of the retrieval performance of SAWSDL-MX2.

scholarly journals Speaker Identification Using Data-Driven Score Classification

2016 ◽  
Vol 21 (2) ◽  
pp. 55-63
Author(s):  
Hock Gan ◽  
Iosif Mporas ◽  
Saeid Safavi ◽  
Reza Sotudeh
Keyword(s):  
Support Vector Machine ◽  
Real World ◽  
Comparative Evaluation ◽  
Speaker Identification ◽  
Selection Task ◽  
Identification Accuracy ◽  
Data Driven ◽  
Support Vector ◽  
Classification Algorithms ◽  
Using Data

Abstract We present a comparative evaluation of different classification algorithms for a fusion engine that is used in a speaker identity selection task. The fusion engine combines the scores from a number of classifiers, which uses the GMM-UBM approach to match speaker identity. The performances of the evaluated classification algorithms were examined in both the text-dependent and text-independent operation modes. The experimental results indicated a significant improvement in terms of speaker identification accuracy, which was approximately 7% and 14.5% for the text-dependent and the text-independent scenarios, respectively. We suggest the use of fusion with a discriminative algorithm such as a Support Vector Machine in a real-world speaker identification application where the text-independent scenario predominates based on the findings.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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