Survey of Machine Learning Algorithms & its Applications
Autor: | Akshada Sunil Shitole, I Priyadarshini |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: | |
DOI: | 10.5281/zenodo.5090569 |
Popis: | Machine Learning is a subset of Artificial Intelligence. Machine learning is one of the latest technologies which has brings new innovations in various fields. Machine learning refers to the concept of train the machine in such a way it can learns from a past experiences or it can learn from a data provided to it. The concept machine learning can be implemented in various fields using its various algorithms. The machine learning contains various algorithms like KNN, K means, decision tree, random forest, support vector machine etc. Machine Learning can be further classified into Supervised Learning, Unsupervised Learning, Reinforcement. Supervised learning performs predictions and Unsupervised learning performs clustering. Further Machine Learning also consists of Deep Learning. Deep learning consists of studies of neural networks. {"references":["Priya, K. L., Kypa, M. S. C. R., Reddy, M. M. S., & Reddy, G. R. M. (2020, June). A Novel Approach to Predict Diabetes by Using Naive Bayes Classifier. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) (pp. 603-607). IEEE.","Bharate, A. A., & Shirdhonkar, M. S. (2020, March). Classification of Grape Leaves using KNN and SVM Classifiers. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 745-749). IEEE..","Rai, A. K., & Dwivedi, R. K. (2020, July). Fraud detection in credit card data using unsupervised machine learning based scheme. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 421-426). IEEE.","Rochmawati, N., Hidayati, H. B., Yamasari, Y., Yustanti, W., Rakhmawati, L., Tjahyaningtijas, H. P., & Anistyasari, Y. (2020, October). Covid Symptom Severity Using Decision Tree. In 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE) (pp. 1-5). IEEE.","Sindhu, S., Patil, S. P., Sreevalsan, A., Rahman, F., & AN, M. S. (2020, October). Phishing Detection using Random Forest, SVM and Neural Network with Backpropagation. In 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) (pp. 391-394). IEEE.","Wadekar, A. (2019, July). Predicting Opioid Use Disorder (OUD) Using A Random Forest. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 1, pp. 960-961). IEEE.","Ray, S. (2019, February). A quick review of machine learning algorithms. In 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 35-39). IEEE..","Pahwa, K., & Agarwal, N. (2019, February). Stock market analysis using supervised machine learning. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 197-200). IEEE.","Li, G., & Zhang, J. (2018, October). Music personalized recommendation system based on improved KNN algorithm. In 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (pp. 777-781). IEEE.","Hori, G. (2018, July). Identifying Factors Contributing to University Dropout with Sparse Logistic Regression. In 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 430-433). IEEE."]} |
Databáze: | OpenAIRE |
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