scholarly journals Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

2014 ◽  
Vol 6 ◽  
pp. 229-236 ◽  
Author(s):  
Ian C. Gould ◽  
Alana M. Shepherd ◽  
Kristin R. Laurens ◽  
Murray J. Cairns ◽  
Vaughan J. Carr ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)

Teknika ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 18-26
Author(s):  
Hendry Cipta Husada ◽  
Adi Suryaputra Paramita
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Support Vector ◽  
Learning Approach ◽  
Social Media Platform ◽  
Machine Learning Approach ◽  
Media Platform ◽  
Fold Cross Validation

Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter 𝐶(complexity) = 10 dan 𝛾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.

Classification of different skarn deposits based on the compositional variability of associated grandite garnets: a data science and Machine Learning approach

2021 ◽  
Author(s):  
Urmi Ghosh ◽  
Tuhin Chakraborty
Keyword(s):  
Machine Learning ◽  
Trace Element ◽  
Data Science ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Skarn Deposits

<p>Rapid technological improvements made in in-situ analysis techniques, including LA-ICPMS, have transformed the field of analytical geochemistry. This has a far-reaching impact for different petrogenetic and ore-genetic studies where minute major and trace element compositional changes between different mineral zones within a single crystal can now be demarcated. Minerals such as garnet although robust are quite sensitive to the changing P-T and fluid conditions during their formation. These minerals have become powerful tools to characterize mineralization types. Previously, Meinert (1992) has used in-situ major element EPMA analysis results to classify different skarn deposit based on the end-member composition of hydrothermal garnets. Alternatively, Tian et al. (2019) used the garnet trace element composition for the similar purpose. However, these discrimination plots/ classification schemes show major overlap in different skarn deposits, such as Fe, Cu, Zn, and Au. The present study is an attempt to use machine learning approach on available garnet data to found a more potent classification scheme for skarn deposits, thus reaffirming garnet as a faithful indicator for hydrothermal ore deposits. We have meticulously collected major and trace element data of Ca-rich garnets, associated with different skarn deposits worldwide from 40 publications. This collected data is then used to train a model for fingerprinting the skarn deposits. Stratified random sampling method has been used on the dataset with 80% of the samples as test set and the rest 20 % as training dataset. We have used K-nearest neighbour (KNN), Support Vector Machine (SVM) and Random Forest algorithms on the data by using Python as a platform. These ML classification algorithm performs better than the earlier existing models available for classification of ore types based on garnet composition in skarn system. Factor importance is calculated that shows which elements play a pivotal role in classification of the ore type. Our results depict that multiple garnet forming elements taken together can reliably be used to discriminate between different ore formation settings.</p>

Driver Recognition on Segway

2012 ◽  
Vol 3 (1) ◽  
pp. 76-88
Author(s):  
Hiroshi Sato ◽  
Julien Rossignol
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Learning Approach ◽  
Statistical Machine Learning ◽  
Recognition Method ◽  
Human Behaviors ◽  
Machine Learning Approach ◽  
User Friendly

Statistical machine learning approach to understand human behaviors has been attracting considerable amounts of attention in recent years. If the authors understand more about humans, the authors can make more user-friendly machines. In this paper, the authors propose the driver recognition method from their record of manipulations using support vector machine. The authors demonstrate the efficiency of the authors’ method using the Segway. The performance of the recognition is quite good especially when the authors introduce the pre-process with FFT.

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