Problem statement: Nowadays, the Internet plays an important role in communication between people. To ensure a secure communication between two parties, we need a security system to detect the attacks very effectively. Network intrusion detection serves as a major system to work with other security system to protect the computer networks. Approach: In this article, an Adaboost algorithm for net...

There are two main approaches to the problem of gender classification, Support Vector Machines (SVMs) and Adaboost learning methods, of which SVMs are better in correct rate but are more computation intensive while Adaboost ones are much faster with slightly worse performance. For possible real-time applications the Adaboost method seems a better choice. However, the existing Adaboost algorithm...

ÐA useful notion of weak dependence between many classifiers constructed with the same training data is introduced. It is shown that if both this weak dependence is low and the expected margins are large, then decison rules based on linear combinations of these classifiers can achieve error rates that decrease exponentially fast. Empirical results with randomized trees and trees constructed via...

One classic approach to real-time object detection is to use adaboost to a train a set of look up tables of discrete features. By utilizing a discrete feature set, from features such as local binary patterns, efficient classifiers can be designed. However, these classifiers include interpolation operations while scaling the images over various scales. In this work, we propose the use of real va...

The task of object tracking is the following: given a video and an identified object (usually given by a bounding box in the first frame), track the position of the object over the frames of the video. Difficulties in this process include occlusion or changes in orientation. The algorithm by Grabner, Grabner, and Bischof [3] is robust against these changes by using a large number of “weak class...

We propose a method of automatic hardware implementation of a decision rule based on the Adaboost algorithm. We review the principles of the classification method and we evaluate its hardware implementation cost in term of FPGA’s slice, using different weak classifiers based on the general concept of hyperrectangle. We show how to combine the weak classifiers in order to find a good trade-off b...

In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...

We consider learning decision trees in the boosting framework, where we assume that the classifiers in each internal node come from a hypothesis class HI which satisfies the weak learning assumption. In this work we consider the class of stochastic linear classifiers for HI , and derive efficient algorithms for minimizing the Gini index for this class, although the problem is non-convex. This i...

Real life classification problems require an investigation of relationships between features in heterogeneous data sets, where different predictive models can be more proper for different regions of the data set. A solution to this problem is the application of the local boosting of weak classifiers ensemble method. A main drawback of this approach is the time that is required at the prediction...

This paper proposes a novel approach for facial expression recognition by boosting Local Binary Patterns (LBP) based classifiers. Low-cost LBP features are introduced to effectively describle local features of face images. A novel learning procedure, Conditional Mutual Information based Boosting (CMIB), is proposed. CMIB learns a sequence of weak classifiers that maximize their mutual informati...