Call to Action: Promoting Domestic and Global Tobacco Control by Ratifying the Framework Convention on Tobacco Control in the United States

Tim K. Mackey and colleagues outline why the United States should ratify the Framework Convention on Tobacco Control (FCTC). Please see later in the article for the Editors' Summar

Eigenconnections to Intrusion Detection

Most current intrusion detection systems are signature based ones or machine learning based methods. Despite the number of machine learning algorithms applied to KDD 99 cup, none of them have introduced a pre-model to reduce the huge information quantity present in the different KDD 99 datasets. We introduce a method that applies to the different datasets before performing any of the different machine learning algorithms applied to KDD 99 intrusion detection cup. This method enables us to significantly reduce the information quantity in the different datasets without loss of information. Our method is based on Principal Component Analysis (PCA). It works by projecting data elements onto a feature space, which is actually a vector space R d , that spans the significant variations among known data elements. We present two well known algorithms we deal with, decision trees and nearest neighbor, and we show the contribution of our approach to alleviate the decision process. We rely on some experiments we perform over network records from the KDD 99 dataset, first by a direct application of these two algorithms on the rough data, second after projection of the different datasets on the new feature space

Feature Weighting for Lazy Learning Algorithms

: Learning algorithms differ in the degree to which they process their inputs prior to their use in performance tasks. Many algorithms eagerly compile input samples and use only the compilations to make decisions. Others are lazy: they perform less precompilation and use the input samples to guide decision making. The performance of many lazy learners significantly degrades when samples are defined by features containing little or misleading information. Distinguishing feature relevance is a critical issue for these algorithms, and many solutions have been developed that assign weights to features. This chapter introduces a categorization framework for feature weighting approaches used in lazy similarity learners and briefly surveys some examples in each category. 1.1 INTRODUCTION Lazy learning algorithms are machine learning algorithms (Mitchell, 1997) that are welcome members of procrastinators anonymous. Purely lazy learners typically display the following characteristics (Aha, 19..