Cs-Lg
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Reinforcement learning for port-Hamiltonian systems
Expectation-Propogation for the Generative Aspect Model
The Interaction of Entropy-Based Discretization and Sample Size: An Empirical Study
Sparse Nonparametric Graphical Models
A Topic Modeling Toolbox Using Belief Propagation
Protein Structure Prediction by Protein Alignments
Discretization of Temporal Data: A Survey
Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices
Sum-Product Networks: A New Deep Architecture
Temporal Autoencoding Restricted Boltzmann Machine
Mining Techniques in Network Security to Enhance Intrusion Detection Systems
Near-Optimal Algorithms for Online Matrix Prediction
Stochastic Low-Rank Kernel Learning for Regression
Learning RoboCup-Keepaway with Kernels
A Comparison Between Data Mining Prediction Algorithms for Fault Detection(Case study: Ahanpishegan co.)
A probabilistic methodology for multilabel classification
A metric learning perspective of SVM: on the relation of SVM and LMNN
Active Learning of Custering with Side Information Using $eps$-Smooth Relative Regret Approximations
On the Lagrangian Biduality of Sparsity Minimization Problems
Gaussian Processes for Sample Efficient Reinforcement Learning with RMAX-like Exploration
Optimized Look-Ahead Tree Policies: A Bridge Between Look-Ahead Tree Policies and Direct Policy Search
