N.  Raghavendra Sai, K.  Satya Rajes, 
    A New Approach for Classification of Network Data using One Class SVM,
    Journal of Engineering and Applied Sciences,
    Volume 15,Issue 17,
    2020,
    Pages 3146-3151,
    ISSN 1816-949x,
    jeasci.2020.3146.3151,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2020.3146.3151)
    Abstract: One class classification recognizes the target
class from all other classes using only preparing data from
the target class. One class order is suitable for those
situations where exceptions are not spoken to well in the
training set. One-class learning or unsupervised SVM,
aims at isolating data from the origin in the
high-dimensional, indicator space (not the original
predictor space) and is an algorithm used for outlier
detection. Support vector machine is a machine learning
method that is widely used for data examining and pattern
recognizing. Support Vector Machines (SVMs, also
Support Vector Networks) are supervised learning models
with related learning algorithms that analyze data and
recognize patterns, used for classification and regression
analysis. In this study, we will review the difference
between both these classes.
    Keywords: SVM;recognizes;algorithm;pattern;high-dimensional