In many datasets, the samples are related by a known image transformation, such as rotation, or a repeatable non-rigid deformation. This applies to both datasets with the same objects under different viewpoints, and datasets augmented with virtual samples. Such datasets possess a high degree of redundancy, because geometrically-induced transformations should preserve intrinsic properties of the...

IGERT 2 Electrical Engineering, California Institute of Technology [email protected] 3 Lab of Neuro Imaging University of California, Los Angeles [email protected] { } { } { } In object recognition in general and in face detection in particular, data alignment is necessary to achieve good classification results with certain statistical learning approaches such as Viola-Jones. Data can ...

The so-called “boosting” principle was introduced by Schapire and Freund in the 1990s in relation to weak learners in the Probably Approximately Correct computational learning framework. Another practice that has developed in recent years consists in assessing the quality of evolutionary or genetic classifiers with Receiver Operating Characteristics (ROC) curves. Following the RankBoost algorit...

The visual detection of robots is a difficult but relevant problem in several robotic applications. In the present article, a framework for the robust and fast visual detection of legged-robots is proposed. This framework uses cascades of nested classifiers, the Adaboost boosting algorithm, and domainpartitioning based weak classifiers. Using the proposed framework, frontal, profile and back de...

Forum Crawler Under Supervision (FoCUS) is a supervised web-scale forum crawler. The web contains large data and innumerable websites that are monitored by a tool or program known as crawler. The goal is to crawl relevant forum content from the web with minimal overhead. Forums have different layouts or styles and are powered by different forum software packages. They have similar implicit navi...

This paper deals with the supervised classification when the response variable is binary and its class distribution is unbalanced. In such situation, it is not possible to build a powerful classifier by using standard methods such as logistic regression, classification tree, discriminant analysis, etc. To overcome this shortcoming of these methods that provide classifiers with low sensibility, ...

We investigate the application of the co-training learning algorithm on the sentence boundary classification problem by using lexical and prosodic information. Co-training is a semisupervised machine learning algorithm that uses multiple weak classifiers with a relatively small amount of labeled data and incrementally uses unlabeled data. The assumption in cotraining is that the classifiers can...

In this paper, we study a generalization of z-ideals in the ring C(X) of continuous real valued functions on a completely regular Hausdorff space X. The notion of a weak ideal and naturally a weak z-ideal and a prime weak ideal are introduced and it turns out that they behave such as z-ideals in C(X).

One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data minin...

To improve weak classifiers bagging and boosting could be used. These techniques are based on combining classifiers. Usually, a simple majority vote or a weighted majority vote are used as combining rules in bagging and boosting. However, other combining rules such as mean, product and average are possible. In this paper, we study bagging and boosting in Linear Discriminant Analysis (LDA) and t...