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 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.

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 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 Vehicle Recognition based on Support Vector Machine

2019 ◽  
Vol 8 (4) ◽  
pp. 9513-9515
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Vehicle Recognition ◽  
Basic Procedure ◽  
Vector Machines ◽  
Made In

These days, there is a colossal progression in zones of computerization and PC vision. Item ID is a basic procedure in these innovations. It distinguishes a particular item from a picture or video arrangement and the move is made in like manner. AI calculations are widely utilized for article ID in different applications. The essential highlights are removed from the pictures and are prepared utilizing different classifiers. This paper proposes an article recognizable proof method utilizing Support Vector Machines (SVM). The proposed framework is contrasted and Decision Tree (DT) and K-Nearest Neighbor (KNN) characterization calculations. The item ID framework is surveyed on ID precision, prevision and review.

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 Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

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