Margin maximizing properties play an important role in the analysis of classi£cation models, such as boosting and support vector machines. Margin maximization is theoretically in...
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized...
Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. N...
This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases. We focus on privacy-preserving logisti...
This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key m...
In this paper we will briefly describe the approaches taken by the Berkeley Cheshire Group for the Adhoc-TEL 2008 tasks (Mono and Bilingual retrieval). Since the AdhocTEL task is ...
In this paper we will briefly describe the approaches taken by Berkeley for the main GeoCLEF 2008 tasks (Mono and Bilingual retrieval). The approach this year used probabilistic t...
There are many applications available for phishing detection. However, unlike predicting spam, there are only few studies that compare machine learning techniques in predicting ph...
Saeed Abu-Nimeh, Dario Nappa, Xinlei Wang, Suku Na...
Abstract. Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and continuous numeric values. F...
Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees ...
Implicit query systems examine a document and automatically conduct searches for the most relevant information. In this paper, we offer three contributions to implicit query resea...