Regularized Semi-paired Kernel CCA for Domain Adaptation
Manual labeling of sufficient training data for diverse application domains is a costly, laborious task and often prohibitive. Therefore,...
Scalable Hybrid Deep Neural Kernel Networks
The best of two worlds: We introduced a hybrid deep neural kernel framework. The proposed deep learning model follows a combination of...
Online Semi-Supervised Classification/Clustering:
- Is obtaining labelled training data costly? - Does the distribution of your data change over time? - Are you dealing with data streams?...
Multi-Label Semi-Supervised Learning using Regularized Kernel Spectral Clustering
- Automatic image annotation - Web page categorization - Protein function prediction - Are you confronted with partially labelled...
Learning Solution of Differential Equations using LSSVM based model
- Machine learning models can learn the trajectories of a dynamical systems in semi-supervised fashion! - Only few training data which...
Multi-Class Semi-Supervised Kernel Spectral Clustering
- Only few labelled but large amount of unlabelled instances are available! - Guiding an unsupervised model with additional expert...
Parameter Estimation of Dynamical System
Parameter estimation plays an important role in modelling of dynamic processes in physics, engineering and biology. An integration-free...
Symbolic Computing of LS-SVM Based Models
A software tool SYM-LS-SVM-SOLVER written in Maple is developed to derive the dual system and the dual model representation of LS-SVM...
Large Scale Semi-Supervised Learning
- Analyzing large scale data sets (e.g 5,000,000 data points) on a laptop scale! - No super-computer is needed! - Tasks: Classification,...
Non-Parallel Semi-Supervised Classification
A non-parallel semi-supervised algorithm based on kernel spectral clustering is formulated. The prior knowledge about the labels is...
