Support Vector Machine Extensions: SVM, RVM, Transductive SVM

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Clustering: Theory and Practice

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Semi-supervised Clustering: Transductive SVM

Explores Transductive Support Vector Machine for semi-supervised clustering, aiming for zero error on labeled points and well-separated unlabeled points.

Clustering: Unsupervised Learning

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Clustering: Unsupervised Learning

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Kernel K-means: Advanced Machine Learning

Introduces Kernel K-means, extending K-means to create non-linear separations of data points.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Unsupervised Learning: PCA & K-means

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Classification: Introduction

Covers clustering, semi-supervised clustering, and binary classification formalization, along with various classification techniques.