SVM and kNN exemplify several important trade-offs in machine learning (ML). SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set ...

With the proliferation of embedded computing, many machine learning (ML) applications have found their way into embedded devices. The SVM classifier is deemed suitable for real-world ML applications, ...

Support Vector Machines (SVMs) is one of the most popular machine learning algorithms as it provides high-performance and needs minimal tuning. It can be used for classification, regression and other ...

This is an accepted paper at the 10th International Conference on Machine Learning and Computing (ICMLC) in Macau, China on February 26-28, 2018. The full paper on this project may be read at ...

To see if the accuracy of bankruptcy prediction can be further improved, we propose three latest models―support vector machine (SVM), neural network, and autoencoder. Support vector machine is a ...

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector Machine (SVM) for Image Classification This project was inspired by Y. Tang's Deep Learning using Linear Support ...

where, x1, x2 = Co-ordinates of one data point. y1, y2 = Co-ordinates of another data point. 2.2. Support vector machine. The supervised learning algorithm Support Vector Machine (SVM), which is ...

Upon integrating the Grey Wolf Optimizer (GWO) and Support Vector Machine (SVM) with the pre-trained CNN frameworks, we observed a significant enhancement in the model performances. The data ...

Specialization: Intro to Statistical Learning Instructor: Osita Onyejeweke, Assistant ProfessorPrior knowledge needed: Intro Statistics and Foundational MathLearning Outcomes Understand the advantages ...