Kernel methods and support vector machines for handwriting recognition

• Main Authors: Ahmad A.R., Khalid M., Yusof R.
• Other Authors: 35589598800
• Format: Conference paper
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Subjects: Artificial intelligence; Character recognition; Classification (of information); Geometry; Learning systems; Quadratic programming; Classification tasks; Handwriting recognition; Higher dimensional features; Kernel function; Kernel methods; Maximal margin; Sequential minimization optimizations; Training data; Support vector machines
• Institution: Universiti Tenaga Nasional

This paper presents a review of kernel methods in machine learning. The support vector machine (SVM) as one of the methods in machine learning to make use of kernels is first discussed with the intention of applying it to handwriting recognition. SVM works by mapping training data for a classification task into a higher dimensional feature space using the kernel function and then finding a maximal margin hyperplane, which separates the mapped data. Finding the solution hyperplane involves using quadratic programming which is computationally intensive. Algorithms for practical implementation such as sequential minimization optimization (SMO) and its improvements are discussed. A few simpler methods similar to SVM but requiring simpler computation are also mentioned for comparison. Usage of SVM for handwriting recognition is then proposed. � 2002 IEEE.