Logistic regression with conjugate gradient descent for document classification

• Main Author: Namburi, Sruthi
• Language: en_US
• Published: Kansas State University 2016
• Subjects: Document Classification; Machine Learning; Logistic Regression
• Online Access: http://hdl.handle.net/2097/32658

Master of Science === Department of Computing and Information Sciences === William H. Hsu === Logistic regression is a model for function estimation that measures the relationship between independent variables and a categorical dependent variable, and by approximating a conditional probabilistic density function using a logistic function, also known as a sigmoidal function. Multinomial logistic regression is used to predict categorical variables where there can be more than two categories or classes. The most common type of algorithm for optimizing the cost function for this model is gradient descent. In this project, I implemented logistic regression using conjugate gradient descent (CGD). I used the 20 Newsgroups data set collected by Ken Lang. I compared the results with those for existing implementations of gradient descent. The conjugate gradient optimization methodology outperforms existing implementations.