পদ্ধতির তুলনা করুন

	Semi-supervised Support Vector Machine	সাপোর্ট ভেক্টর মেশিন (শ্রেণীকরণ)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1999	1995
প্রবর্তক	Joachims, T.	Cortes, C. & Vapnik, V.
ধরন	Semi-supervised classifier	Maximum-margin classifier (kernel method)
মৌলিক উৎস	Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
অপর নাম	S3VM, Transductive SVM, TSVM, Semi-SVM	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
সম্পর্কিত	4	5
সারসংক্ষেপ	Semi-supervised Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution, yielding better generalization when labeled samples are scarce.	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.