The problem of classification has been widely studied in the data mining, machine learning,
database, and information retrieval communities with applications in a number of diverse …

Logistic regression analysis of high-dimensional data, such as natural language text, poses
computational and statistical challenges. Maximum likelihood estimation often fails in these …

The explosion of user-generated content on the Web has led to new opportunities and
significant challenges for companies, that are increasingly concerned about monitoring the …

It is difficult to apply machine learning to new domains because often we lack labeled
problem instances. In this paper, we provide a solution to this problem that leverages …

The goal of sentiment prediction is to automatically identify whether a given piece of text
expresses positive or negative opinion towards a topic of interest. One can pose sentiment …

A content-based personalized recommendation system learns user specific profiles from
user feedback so that it can deliver information tailored to each individual user's interest. A …

In many information processing tasks, labels are usually expensive and the unlabeled data
points are abundant. To reduce the cost on collecting labels, it is crucial to predict which …

This chapter focuses on the interaction between active learning (AL) and class imbalance,
discussing (i) AL techniques designed specifically for dealing with imbalanced settings,(ii) …

ABSTRACT Recent Machine Learning algorithms are widely available for various purposes.
But which classifier is suitable for particular data is not yet defined. To consider this into …

Standard machine learning techniques typically require ample training data in the form of
labeled instances. In many situations it may be too tedious or costly to obtain sufficient …