Dimension Reduction via Unsupervised Learning Yields Significant Computational Improvements for Support Vector Machine Based Protein Family Classification

2008 ◽  
Author(s):  
Bobbie-Jo M. Webb-Robertson ◽  
Melissa M. Matzke ◽  
Christopher S. Oehmen
Keyword(s):  
Support Vector Machine ◽  
Dimension Reduction ◽  
Unsupervised Learning ◽  
Protein Family ◽  
Support Vector ◽  
Family Classification

scholarly journals Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter

2019 ◽  
Vol 6 (2) ◽  
pp. 226-235
Author(s):  
Muhammad Rangga Aziz Nasution ◽  
Mardhiya Hayaty
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Fold Cross Validation

Salah satu cabang ilmu komputer yaitu pembelajaran mesin (machine learning) menjadi tren dalam beberapa waktu terakhir. Pembelajaran mesin bekerja dengan memanfaatkan data dan algoritma untuk membuat model dengan pola dari kumpulan data tersebut. Selain itu, pembelajaran mesin juga mempelajari bagaimama model yang telah dibuat dapat memprediksi keluaran (output) berdasarkan pola yang ada. Terdapat dua jenis metode pembelajaran mesin yang dapat digunakan untuk analisis sentimen:  supervised learning dan unsupervised learning. Penelitian ini akan membandingkan dua algoritma klasifikasi yang termasuk dari supervised learning: algoritma K-Nearest Neighbor dan Support Vector Machine, dengan cara membuat model dari masing-masing algoritma dengan objek teks sentimen. Perbandingan dilakukan untuk mengetahui algoritma mana lebih baik dalam segi akurasi dan waktu proses. Hasil pada perhitungan akurasi menunjukkan bahwa metode Support Vector Machine lebih unggul dengan nilai 89,70% tanpa K-Fold Cross Validation dan 88,76% dengan K-Fold Cross Validation. Sedangkan pada perhitungan waktu proses metode K-Nearest Neighbor lebih unggul dengan waktu proses 0.0160s tanpa K-Fold Cross Validation dan 0.1505s dengan K-Fold Cross Validation.

Using Eye-Tracking and Support Vector Machine to Measure Learning Attention in eLearning

2013 ◽  
Vol 311 ◽  
pp. 9-14 ◽  
Author(s):  
Chien Hung Liu ◽  
Po Yin Chang ◽  
Chun Yuan Huang
Keyword(s):  
Support Vector Machine ◽  
Learning Environment ◽  
Unsupervised Learning ◽  
Real Time ◽  
Feedback System ◽  
Integrated Approach ◽  
Support Vector ◽  
Region Detection ◽  
Volumetric Features ◽  
Finite State

For eLearning, how to naturally measure the learning attention of students with lower cost devices in an unsupervised learning environment is a crucial issue. Students often far away and out of teachers’ control in above situation which may cause students do not have strong learning motivation and might feel fatigued and inattentive for learning. A real-time and naturally learning attention measure approach can support instructor to better control the learning attention of students in unsupervised learning environment. This paper proposes an integrated approach, named Real-time Learning Attention Feedback System (RLAFS) which could naturally measure learning attention in unsupervised learning environments. The system architecture of RLAFS consists with three layers: first layer is Image preprocessing layer, which is responsible for image processing and motion detection. Second is eyebrow region detection layer, which is focus on the features of face and eyes capturing and positioning. Classifier layer is the third layer, in which integral image, volumetric features and finite-state-machine are used to capture the current state of learning attention of students. Consequently, support vector machine is utilized to classify the level of learning attention. The experiments are conducted in an unsupervised environment, and results showed RLAFS is a promising approach which can naturally measure learning attention and has a significant impact on learning efficient.

scholarly journals Optimasi Textblob Menggunakan Support Vector Machine Untuk Analisis Sentimen (Studi Kasus Layanan Telkomsel)

2021 ◽  
Vol 10 (1) ◽  
pp. 13-15
Author(s):  
Kevin Perdana ◽  
Titania Pricillia ◽  
Zulfachmi
Keyword(s):  
Support Vector Machine ◽  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
A Value ◽  
Processing Techniques

Sentiment analysis refers to Natural Language Processing techniques that are classified as Unsupervised Learning to identify positive, negative, or neutral opinions. Many of these opinions come through Twitter, because social media is quite effective and efficient in commenting because it can only write a maximum of 140 characters. From previous research, the value of the accuracy of the sentiment analysis carried out by one of the NLP libraries, namely TextBlob, has shown that Unsupervised Learning does not produce such good scores. With the Telkomsel service case study the writer took the dataset from Twitter and the results of the analysis with TextBlob only showed a value of 58.59%. Optimization is done by adding the Support Vector Machine method which is included in the Supervised Learning category. The best results obtained from this study are values that show 75%.

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