Investigation for using different penalty functions (L1 absolute value penalty or lasso, L2 standard weight decay or ridge regression, weight elimination etc.) on the weights for logistic regression for classification. 5 data sets from UCI Machine Learning Repository were used.

A frequent problem in estimating logistic regression models is a failure of the likelihood maximization algorithm to converge. In most cases, this failure is a consequence of data patterns known as complete or quasi-complete separation. For these patterns, the maximum likelihood estimates simply do not exist. In this paper, I examine how and why complete or quasi-complete separation occur, and ...

Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm...

A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each. This paper observes that if the inference problem is “smoothed” through the addition of entropy terms, for fixed messages, the learning objective reduces to a traditional (non-structured) logistic regression problem...

As time passes, purchasing habits of consumers have been changing constantly. Consumption has become a phenomenon that turned into madness due to the acceleration aggressive sales enhancing efforts based on fierce competition and fact confuse concepts need desire. Confronting with easily reachable products broad distribution channels, markets are vital institutions for people. The first aim thi...

ACTIVE LEARNING FOR LOGISTIC REGRESSION Andrew Ian Schein Supervisor: Lyle H. Ungar Which active learning methods can we expect to yield good performance in learning logistic regression classifiers? Addressing this question is a natural first step in providing robust solutions for active learning across a wide variety of exponential models including maximum entropy, generalized linear, loglinea...