Learn · Neural Networks and Applications
Lecture 24 of 37Neural Networks and Applications
Radial Basis Function Networks: Cover's Theorem
Prof. Somnath Sengupta · IIT Kharagpur
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All the lectures
- Introduction to Artificial Neural Networks
- Artificial Neuron Model and Linear Regression
- Gradient Descent Algorithm
- Nonlinear Activation Units and Learning Mechanisms
- Learning Mechanisms-Hebbian,Competitive,Boltzmann
- Associative memory
- Associative Memory Model
- Condition for Perfect Recall in Associative Memory
- Statistical Aspects of Learning
- V.C. Dimensions: Typical Examples
- Importance of V.C. Dimensions Structural Risk Minimization
- Single-Layer Perceptions
- Unconstrained Optimization: Gauss-Newton's Method
- Linear Least Squares Filters
- Least Mean Squares Algorithm
- Perceptron Convergence Theorem
- Bayes Classifier & Perceptron: An Analogy
- Bayes Classifier for Gaussian Distribution
- Back Propagation Algorithm
- Practical Consideration in Back Propagation Algorithm
- Solution of Non-Linearly Separable Problems Using MLP
- Heuristics For Back-Propagation
- Multi-Class Classification Using Multi-layered Perceptrons
- Radial Basis Function Networks: Cover's Theorem
- Radial Basis Function Networks: Separability & Interpolation
- Posed Surface Reconstruction
- Solution of Regularization Equation: Greens Function
- Use of Greens Function in Regularization Networks
- Regularization Networks and Generalized RBF
- Comparison Between MLP and RBF
- Learning Mechanisms in RBF
- Introduction to Principal Components and Analysis
- Dimensionality reduction Using PCA
- Hebbian-Based Principal Component Analysis
- Introduction to Self Organizing Maps
- Cooperative and Adaptive Processes in SOM
- Vector-Quantization Using SOM