পদ্ধতির তুলনা করুন

	রৈখিক বৈষম্যমূলক বিশ্লেষণ (LDA	সাপোর্ট ভেক্টর মেশিন (শ্রেণীকরণ)
ক্ষেত্র	পরিসংখ্যান	যন্ত্র শিখন
পরিবার	Hypothesis test	Machine learning
উদ্ভবের বছর	1936	1995
প্রবর্তক	Ronald A. Fisher	Cortes, C. & Vapnik, V.
ধরন	Parametric linear classifier / dimensionality reduction	Maximum-margin classifier (kernel method)
মৌলিক উৎস	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
অপর নাম	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
সম্পর্কিত	7	5
সারসংক্ষেপ	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.