Experimentation with novelty detection
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Updated
Jan 28, 2018 - Python
Experimentation with novelty detection
Parse, Compute Pairwaise Similarity Matrices, Train and Test using KNN Classification Algorithm
Kmeans and SOM clustering for 20newsgroup
Naive Bayes classifier and boolean retrieval done on the 20Newsgroups dataset that has been written from scratch. Extremely lightweight and produces decent results. Also currently working on classification using word embeddings.
Cluster labelling was done by using the power of wikipedia search
Text classification using Multinomial NB on 20_newsgroups dataset.
In this project we will generate the sentences using ngrams
This repository contains notebooks which explores the tsne algorithm by applying it on various datasets
Implemented Naive Bayes text classifier for the 20newsgroups dataset
Some hidden knowledge found in the 20 Newsgroups dataset
Assignment 2 – Dimensionality reduction and text classification: converted news text into a machine readable representation, reduced the dimensions of the text representation and trained classifiers to decide which of 20 news groups a sample belongs to.
Clean corpus generic script made with tm package
This repository contains code for our project work as part of the E0-334 Deep Learning for Natural Language Processing course at IISc, Bengaluru. We had proposed a graph-based model for text classification.
Project work as part of the E0-334 Deep Learning for Natural Language Processing course at IISc, Bengaluru. We had proposed a graph-based model for text classification.
We created a topic modeling pipeline to evaluate different topic modeling algorithms, including their performance on short and long text, preprocessed and not preprocessed datasets, and with different embedding models. Finally, we summarized the results and suggested how to choose algorithms based on the task.
FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well in noisy and contaminated datasets.
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