This chapter provides an introduction to the machine learning algorithms along with the optimization techniques. We first introduce support vector machines (SVMs) followed by neural networks and their variants. The applications considered as basis to illustrate these algorithms are the classification problems.

Chapter Contents:

• 4.1 SVM-based machine learning
• 4.2 Single-layer feedforward neural network-based machine learning
• 4.2.1 Single-layer feedforward network
• 4.2.1.1 Feature extraction
• 4.2.1.2 Neural network-based learning
• 4.2.1.3 Incremental LS solver-based learning
• 4.2.2 L2-norm-gradient-based learning
• 4.2.2.1 Multilayer neural network
• 4.2.2.2 Direct-gradient-based L2-norm optimization
• 4.3 DCNN-based machine learning
• 4.3.1 Deep learning for multilayer neural network
• 4.3.2 Convolutional neural network
• 4.3.3 Binary convolutional neural network
• 4.3.3.1 Bitwise convolution
• 4.3.3.2 Bitwise batch normalization
• 4.3.3.3 Bitwise pooling and activation functions
• 4.3.3.4 Bitwise CNN model overview
• 4.4 TNN-based machine learning
• 4.4.1 Tensor-train decomposition and compression
• 4.4.2 Tensor-train-based neural network
• 4.4.3 Training TNN

Inspec keywords: optimisation; support vector machines; neural nets; pattern classification; learning (artificial intelligence)

Other keywords: neural networks; support vector machines; optimization techniques; classification problems; SVM; machine learning algorithms

Subjects: Data handling techniques; Support vector machines; Optimisation techniques; Neural nets; Other topics in statistics