Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Home

Lecture

Support Vector Machine Extensions: SVM, RVM, Transductive SVM

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (37)

Dimensionality Reduction: PCA and LDA Log in to Mediaspace to watch this video

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Introduction to Image Classification Log in to Mediaspace to watch this video

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Clustering & Density Estimation Log in to Mediaspace to watch this video

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Clustering & Density Estimation Log in to Mediaspace to watch this video

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Unsupervised Learning: PCA & K-means Log in to Mediaspace to watch this video

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

Machine Learning Fundamentals: Structure Discovery, Classification, Regression Log in to Mediaspace to watch this video

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

K-means Algorithm Log in to Mediaspace to watch this video

Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.

Reinforcement Learning Concepts Log in to Mediaspace to watch this video

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Kernel Methods: SVM and Regression Log in to Mediaspace to watch this video

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Support Vector Machines: Theory and Applications Log in to Mediaspace to watch this video

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

Unsupervised Learning: Clustering & Dimensionality Reduction Log in to Mediaspace to watch this video

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Unsupervised Learning: Dimensionality Reduction and Clustering Log in to Mediaspace to watch this video

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Feature Expansion: Kernels and KNN Log in to Mediaspace to watch this video

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

File Organization and Indexing Log in to Mediaspace to watch this video

Explores file organization, record formats, indexing techniques, and index classifications in databases, emphasizing the importance of efficient data storage and access.

Deep Learning: Graphs and Transformers Overview Log in to Mediaspace to watch this video

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Deep Learning: Data Representations and Neural Networks Log in to Mediaspace to watch this video

Explores data representations, histograms, neural networks, and deep learning concepts.

General Conics and Homogeneous Coordinates Log in to Mediaspace to watch this video

Covers general conics, homogeneous coordinates, conic recognition, and matrix decomposition.

Page 2 of 2