Improvements to the SMO algorithm for SVM regression

Shevade, Shirish; Keerthi, S. Sathiya; Bhattacharyya, Chiranjib; Murthy, K. R. K.

doi:10.1109/72.870050

Cited by 768 publications

(350 citation statements)

References 4 publications

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“…17 A series of important results related to SMO are due to S. Keerthi and his group. In Keerthi et al (1999) and Shevade et al (1999) they address the threshold computation in SMO. They present examples on which SMO spends a significant amount of time at the end of training trying to establish the single optimal threshold b which is involved in the Osuna-style KKT conditions (c.f.…”

Section: Discussionmentioning

confidence: 99%

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Laskov

2002

Machine Learning

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It is proved that the two algorithms are equivalent for the pattern recognition SVM, and the feasible direction interpretation of the maximal inconsistency algorithm is given for the regression SVM. The experimental results demonstrate an order of magnitude decrease of training time in comparison with training without decomposition, and, most importantly, provide experimental evidence of the linear convergence rate of the feasible direction decomposition algorithms.

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Section: Discussionmentioning

confidence: 99%

Untitled

Laskov

2002

Machine Learning

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“…At the heart of the method is a Support Vector Regression (SVR) model, for which we use the Weka implementation (Frank et al, 2016;Shevade et al, 2000). To provide features that describe the news headline, all headlines are preprocessed using the Stanford CoreNLP library.…”

Section: Methodsmentioning

confidence: 99%

COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines

Schouten¹,

Frăsincar²,

Jong³

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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This paper describes our submission to Task 5 of SemEval 2017, Fine-Grained Sentiment Analysis on Financial Microblogs and News, where we limit ourselves to performing sentiment analysis on news headlines only (track 2). The approach presented in this paper uses a Support Vector Machine to do the required regression, and besides unigrams and a sentiment tool, we use various ontologybased features. To this end we created a domain ontology that models various concepts from the financial domain. This allows us to model the sentiment of actions depending on which entity they are affecting (e.g., decreasing debt is positive, but decreasing profit is negative). The presented approach yielded a cosine distance of 0.6810 on the official test data, resulting in the 12th position.

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“… Support Vector Machine: -SVMs, being computationally powerful tools for supervised learning, are widely used in classification [10,11].…”

Section: Neural Net (Mlp)mentioning

confidence: 99%

Student Performance Prediction Model using Machine Learning Approach: The Case of Wolkite University

Belachew¹,

Gobena²

2017

IJARCSSE

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Abstract-A high prediction accuracy of the students' performance is helpful to identify the low performance students at the beginning of the learning process. Machine learning is used to attain this objective. Machine learning techniques are used to discover models or patterns of data, and it is helpful in the decision-making. The ability to predict performance of students is very crucial in our present education system. We applied Machine learning concepts for this study. The dataset used in our study is taken from the Wolkite university registries office for college of computing and informatics from 2004 up to 2007 E.C with respect to each department. In this study, we have been collected student's transcript data that included their final GPA and their grades in all courses. After pre-processing the data, we applied the machine learning methods, neural networks, Naive Bayesian and Support Vector Machine (SMO). Finally, we built the model for each method, evaluate the performance and compare the results of each model. Using machine learning, the aim was to develop a model which can derive the conclusion on students' academic success.

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Improvements to the SMO algorithm for SVM regression

Cited by 768 publications

References 4 publications

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COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines

Student Performance Prediction Model using Machine Learning Approach: The Case of Wolkite University

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