000			01738cam a2200349 a 4500
003			EG-GiCUC
005			20250223030339.0
008			110120s2010 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.12.17.M.Sc.2010.Ne.H
100	0		_aNermeen Kamel Abdelmoniem
245	1	0	_aHybrid optimization techniques for cancer diagnosis models / _cNermeen Kamel Abdelmoniem ; Supervised L . F . Abdelal , N . H . Sweilam , A . A . Tharwat
246	1	5	_aالتقنيات الامثلية المهجنه لتشخيص مرض السرطان
260			_aCairo : _bNermeen Kamel Abdelmoniem , _c2010
300			_a90P. : _bcharts , facsimiles ; _c25cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Science - Department of Mathematics
520			_aSuport vector machine has become an increasingly popular tool for machine learning tasks involving classification regression or novelty detection . Training a support vector machine requires the solution of a very large quadratic programming problem . Ttaditional optimization methods cannot be directly applied due to memory restrictions . Up to now several approaches exist for circumventing the above shortcomings and work well
530			_aIssued also as CD
653		4	_aCancer model
653		4	_aParticle swarm optimisation (PSO)
653		4	_aSupport vector machine (SVM)
700	0		_aAssem Abdelfatah Tharwat , _eSupervisor
700	0		_aLaila Fahmy Abdelal , _eSupervisor
700	0		_aNasser Hassen Sweilam , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSoheir _eCataloger
942			_2ddc _cTH
999			_c32791 _d32791