Classifier Essay - 890 Words

Name: Sungkeun Kim UIN: 325003839 CSCE689 Paper Report #5 1. Paper Bibliography Information Title: Design and Evaluation of Classifier for Identifying Dactylology Videos in Video Sharing Sites Proceedings of ACM SIGACCESS Conference on Computers and Accessibility, 2012 Authors: - Caio D.D. Monteiro, Department of Computer Science and Engineering - Gutierrez-Osuna, Department of Computer Science and Engineering - Frank M. Shipman, Department of Computer Science and Engineering 2. Summary 2.1 Problem Statement We can be provided by online media a chance for the set and usage of dactylology presentations about various subjects. Nowadays, online media such as locating dactylology videos is based on the existence and correctness of tags,†¦show more content†¦In that the complexity of the problem, most of these researches believe the realizing of finger feature and orientation are insufficient to convert the dactylology. The meaning of American the language is defined by a combination of five features. That is, the shape of the finger, the location of the finger, the palm location of the finger, the direction of motion of the finger. Finally, the facial expression. Without all five components, the conversion of American the language is impossible. In the author’s work, the goal of his research is identifying dactylology video found in video sharing web-sites. This ability would immediately allow people to limit their searches within the videos to those in dactylology. About zero point five percentage of the united states population is deaf and understand words shouted in the better ear. For many people that become deaf in life, dactylology is their way to communication. Combined with the formative era of the brain’s development, this means that the average reading and writing ability among people of the deaf median reading comprehension. For the online to more fully support this group, information is needed to available in dactylology. The author analyzed each video by a video computing system and developed with open formworks which is an open source toolkit. 2.3 Results/Findings The author tested the classifierShow MoreRelatedEssay On Machine Learning Classifiers And Feature Extractors1166 Words   |  5 Pagesanalyse the sentiments using machine learning classifiers and feature extractors. The machine learning classifiers are Naive Bayes, Maximum Entropy and Support Vector Machines (SVM). The feature extractors are unigrams and unigrams with weighted positive and negative keywords. We build a framework that treats classifiers and feature extractors as two distinct components. 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