Outfit of Exemplar-SVMs for Object Detection and Beyond

Yaswanth, V.; Kumar, K. Suresh; N., Harshith; Teja, G Sai; Aparna, R.

doi:10.17485/ijst/2016/v9i30/98720

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Also, machine learning techniques can be applied for the visualizing purpose, for instance in 11 proposed an enhanced SVM which integrates image process and data mining algorithms to analyze brain tumor. Another example of applying machine learning techniques for image processing 12 . Similar to the previous work the use of SVM helps to identify the object from the image.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Classifiers: Evaluation of the Performance in Online Reviews

Pak¹,

Teh²

2016

Indian Journal of Science and Technology

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Objectives: This paper aims to evaluate the performance of the machine learning classifiers and identify the most suitable classifier for classifying sentiment value. The term "sentiment value" in this study is referring to the polarity (positive, negative or neutral) of the text. Methods/Analysis: This work applies machine learning classifiers from WEKA (Waikato Environment for Knowledge Analysis) toolkit in order to perform their evaluation. WEKA toolkit is a great set of tools for data mining and classification. The performance of the machine learning classifiers was measured by examining overall accuracy, recall, precision, kappa statistic and applying few visualization techniques. Finally, the analysis is applied to find the most suitable classifier for classifying sentiment value. Findings: Results show that two classifiers from Rules and Trees categories of classifiers perform equally best comparing to the other classifiers from categories, such as Bayes, Functions, Lazy and Meta. Novelty /Improvement: This paper explores the performance of machine learning classifiers in sentiment value classification in the online reviews. Data used is never been used before to explore the performance of machine learning classifiers.

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Section: Introductionmentioning

confidence: 99%