Support Vector Machines Illuminated

2011 ◽  
pp. 1071-1076 ◽  
Author(s):  
David R. Musicant
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Data Storage ◽  
Research Data ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Vector Machines ◽  
A Current

In recent years, massive quantities of business and research data have been collected and stored, partly due to the plummeting cost of data storage. Much interest has therefore arisen in how to mine this data to provide useful information. Data mining as a discipline shares much in common with machine learning and statistics, as all of these endeavors aim to make predictions about data as well as to better understand the patterns that can be found in a particular dataset. The support vector machine (SVM) is a current machine learning technique that performs quite well in solving common data mining problems.

scholarly journals Research on Parallel Support Vector Machine Based on Spark Big Data Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Huimin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lambda Architecture ◽  
Vector Machines ◽  
Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

scholarly journals PEMILIHAN MODEL PREDIKSI INDEKS HARGA SAHAM YANG DIKEMBANGKAN BERDASARKAN ALGORITMA SUPPORT VECTOR MACHINE(SVM) ATAU MULTILAYER PERCEPTRON(MLP) STUDI KASUS : SAHAM PT TELEKOMUNIKASI INDONESIA TBK

2019 ◽  
Vol 5 (1) ◽  
pp. 29-35
Author(s):  
Rusma Insan Nurachim
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Multilayer Perceptron ◽  
Support Vector ◽  
Rescaled Range ◽  
Vector Machines

Dalam memprediksi suatu kondisi harga saham,beberapa model analisa teknik telah dipakai dandikembangkan. Salah satunya dengan model datamining. Data mining merupakan salah satu cabangilmu komputer yang mencakup database, kecerdasanbuatan (artificial intelligence), statistik dan sebagainya.Penelitian ini melakukan analisis teknikal, yaitu diawalidengan mencari sifat multifraktal pada return sahamobjek penelitian dengan analisis rescaled range (untukmendapatkan eksponen hurst) untuk mengetahui apakahdata return tersebut bersifat acak atau terdapatpengulangan trend. Berikutnya akan dilakukan prediksiterhadap return saham tersebut dengan metode SVM(Support Vector Machines) dan MLP (MultilayerPerceptron) untuk kemudian akan dilakukan komparasimetode mana yang memiliki kesalahan lebih kecildalam memprediksi indeks harga saham.

scholarly journals The Sparse Learning of The Support Vector Machine

2021 ◽  
Vol 2078 (1) ◽  
pp. 012006
Author(s):  
Lipeng Cui ◽  
Jie Shen ◽  
Song Yao
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Sparse Learning ◽  
Support Vector ◽  
Research Directions ◽  
Sparse Model ◽  
Vector Machines ◽  
Fitting Problem

Abstract The sparse model plays an important role in many aeras, such as in the machine learning, image processing and signal processing. The sparse model has the ability of variable selection, so they can solve the over-fitting problem. The sparse model can be introduced into the field of support vector machine in order to get classification of the labels and sparsity of the variables simultaneously. This paper summarizes various sparse support vector machines. Finally, we revealed the research directions of the sparse support vector machines in the future.

scholarly journals Applying support-vector machine learning algorithms toward predicting host–guest interactions with cucurbit[7]uril

2020 ◽  
Vol 22 (26) ◽  
pp. 14976-14982
Author(s):  
Anthony Tabet ◽  
Thomas Gebhart ◽  
Guanglu Wu ◽  
Charlie Readman ◽  
Merrick Pierson Smela ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Small Molecules ◽  
Binding Constant ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Equilibrium Binding ◽  
Vector Machines ◽  
Equilibrium Binding Constant

We evaluate the ability of support-vector machines to predict the equilibrium binding constant of small molecules to cucurbit[7]uril.

scholarly journals Uma Introdução às Support Vector Machines

2007 ◽  
Vol 14 (2) ◽  
pp. 43-67 ◽  
Author(s):  
Ana Carolina Lorena ◽  
André C. P. L. F. De Carvalho
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machines ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Vector Machines ◽  
Learning Technique

This paper presents an introduction to the Support Vector Machines (SVMs), a Machine Learning technique that has received increasing attention in the last years. The SVMs have been applied to several pattern recognition tasks, obtaining results superior to those of other learning techniques in various applications.

scholarly journals KLASIFIKASI HABITAT BENTIK BERBASIS OBJEK DENGAN ALGORITMA SUPPORT VECTOR MACHINES DAN DECISION TREE MENGGUNAKAN CITRA MULTISPEKTRAL SPOT-7 DI PULAU HARAPAN DAN PULAU KELAPA

2018 ◽  
Vol 10 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Nico Wantona Prabowo ◽  
Vincentius P. Siregar ◽  
Syamsul Bahri Agus
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Decision Tree ◽  
Support Vector ◽  
Vector Machines ◽  
Multiresolution Segmentation

Teknik klasifikasi berbasis objek dengan algoritma machine learning SVM untuk citra resolusi tinggi di Indonesia sampai saat ini masih terbatas khususnya untuk pemetaan terumbu karang, oleh karena itu diperlukan kajian lebih lanjut mengenai perbandingan metode maupun penerapan algoritma sebagai alternatif dari proses klasifikasi. Penelitian ini bertujuan memetakan habitat bentik berdasarkan klasifikasi menggunakan metode OBIA dengan algoritma support vector machine dan decision tree di Pulau Harapan dan Kelapa. Segmentasi dilakukan menggunakan algoritma multiresolution segmentation dengan faktor skala 15. Metode OBIA diterapkan pada citra terkoreksi atmosfer dengan skema klasifikasi habitat bentik yang telah ditentukan sebelumnya. Akurasi keseluruhan dari penerapan algoritma SVM dan DT masing-masing sebesar 75,11% dan 60,34%. Analisis nilai Z statistik yang diperoleh dari penerapan dua algoritma yang digunakan yakni sebesar 2,23, dimana nilai ini menunjukkan bahwa klasifikasi dengan algoritma SVM berbeda nyata dengan hasil dari penggunaan algoritma DT.  

scholarly journals Proposta de metodologia para a criação de etiqueta de classificação – estudo de caso: desempenho escolar

2016 ◽  
Vol 23 (1) ◽  
pp. 177-191
Author(s):  
Anderson Roges Teixeira Góes ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Support Vector ◽  
Vector Machines

Resumo A qualidade na educação tem sido objeto de muita discussão, seja nas escolas e entre seus gestores, seja na mídia ou na literatura. No entanto, uma análise mais profunda na literatura parece não indicar técnicas que explorem bancos de dados com a finalidade de obter classificações para o desempenho escolar, nem tampouco há um consenso sobre o que seja “qualidade educacional”. Diante deste contexto, neste artigo, é proposta uma metodologia que se enquadra no processo KDD (Knowledge Discovery in Databases, ou seja, Descoberta de Conhecimento em Bases de Dados) para a classificação do desempenho de instituições de ensino, de forma comparativa, com base nas notas obtidas na Prova Brasil, um dos itens integrantes do Índice de Desenvolvimento da Educação Básica (IDEB) no Brasil. Para ilustrar a metodologia, esta foi aplicada às escolas públicas municipais de Araucária, PR, região metropolitana de Curitiba, PR, num total de 17, que, por ocasião da pesquisa, ofertavam Ensino Fundamental, considerando as notas obtidas pela totalidade dos alunos dos anos iniciais (1º. ao 5º. ano do ensino fundamental) e dos anos finais (6º. ao 9º. ano do ensino fundamental). Na etapa de Data Mining, principal etapa do processo KDD, foram utilizadas três técnicas de forma comparativa para o Reconhecimento de Padrões: Redes Neurais Artificiais; Support Vector Machines; e Algoritmos Genéticos. Essas técnicas apresentaram resultados satisfatórios na classificação das escolas, representados por meio de uma “Etiqueta de Classificação do Desempenho”. Por meio desta etiqueta, os gestores educacionais poderão ter melhor base para definir as medidas a serem adotadas junto a cada escola, podendo definir mais claramente as metas a serem cumpridas.

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