Support Vector Machine in Novelty Detection for Multi-channel Combustion Data

2006 ◽  
pp. 836-843 ◽  
Author(s):  
Lei A. Clifton ◽  
Hujun Yin ◽  
Yang Zhang
Keyword(s):  
Support Vector Machine ◽  
Novelty Detection ◽  
Support Vector

RANKING-BASED KERNELS IN APPLIED BIOMEDICAL DIAGNOSTICS USING A SUPPORT VECTOR MACHINE

2011 ◽  
Vol 21 (06) ◽  
pp. 459-473 ◽  
Author(s):  
VILEN JUMUTC ◽  
PAWEL ZAYAKIN ◽  
ARKADY BORISOV
Keyword(s):  
Support Vector Machine ◽  
Novelty Detection ◽  
Clinical Diagnostics ◽  
High Dimensional ◽  
Support Vector ◽  
Biomedical Data ◽  
Classification Rate ◽  
Generalization Bounds ◽  
Theoretical Generalization ◽  
Biomedical Diagnostics

This paper presents some essential findings and results on using ranking-based kernels for the analysis and utilization of high dimensional and noisy biomedical data in applied clinical diagnostics. We claim that presented kernels combined with a state-of-the-art classification technique — a Support Vector Machine (SVM) — could significantly improve the classification rate and predictive power of the wrapper method, e.g. SVM. Moreover, the advantage of such kernels could be potentially exploited for other kernel methods and essential computer-aided tasks such as novelty detection and clustering. Our experimental results and theoretical generalization bounds imply that ranking-based kernels outperform other traditionally employed SVM kernels on high dimensional biomedical and microarray data.

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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