  Authorship attribution mainly deals with undecided authorship of literary
texts. Authorship attribution is useful in resolving issues like uncertain
authorship, recognize authorship of unknown texts, spot plagiarism so on.
Statistical methods can be used to set apart the approach of an author
numerically. The basic methodologies that are made use in computational
stylometry are word length, sentence length, vocabulary affluence, frequencies
etc. Each author has an inborn style of writing, which is particular to
himself. Statistical quantitative techniques can be used to differentiate the
approach of an author in a numerical way. The problem can be broken down into
three sub problems as author identification, author characterization and
similarity detection. The steps involved are pre-processing, extracting
features, classification and author identification. For this different
classifiers can be used. Here fuzzy learning classifier and SVM are used. After
author identification the SVM was found to have more accuracy than Fuzzy
classifier. Later combined the classifiers to obtain a better accuracy when
compared to individual SVM and fuzzy classifier.
