التقنيات الامثلية المهجنة لتشخيص مرض السرطان

Abstract

Support vector machine has become an increasingly popular tool for machine learning tasks involv­ ing classification, regression or novelty detection. Training a Support Vector Machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumvent­ ing the above shortcomings and work well. One ot these approachs is, another learning algorithm, Particle Swarm Optimization, Quantum-behave Particle Swarm for training SVM is also introduced. Another approach named Least Square Support Vector machine and Active Set Strategy are intro­ duced. These methods are tested on Breast Cancer Dataset and compared with the exact solution model problem.