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» Boosting Technique for Combining Cellular GP Classifiers
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ECCV
2004
Springer
14 years 9 months ago
Weak Hypotheses and Boosting for Generic Object Detection and Recognition
In this paper we describe the first stage of a new learning system for object detection and recognition. For our system we propose Boosting [5] as the underlying learning technique...
Andreas Opelt, Michael Fussenegger, Axel Pinz, Pet...
TSMC
2008
198views more  TSMC 2008»
13 years 7 months ago
Representation Plurality and Fusion for 3-D Face Recognition
In this paper, we present an extensive study of 3-D face recognition algorithms and examine the benefits of various score-, rank-, and decision-level fusion rules. We investigate f...
Berk Gökberk, Helin Dutagaci, A. Ulas, Lale A...
ICONIP
2008
13 years 8 months ago
An Evaluation of Machine Learning-Based Methods for Detection of Phishing Sites
In this paper, we present the performance of machine learning-based methods for detection of phishing sites. We employ 9 machine learning techniques including AdaBoost, Bagging, S...
Daisuke Miyamoto, Hiroaki Hazeyama, Youki Kadobaya...
RAS
2010
167views more  RAS 2010»
13 years 5 months ago
Data association and occlusion handling for vision-based people tracking by mobile robots
This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an ad...
Grzegorz Cielniak, Tom Duckett, Achim J. Lilientha...
PAMI
2012
11 years 9 months ago
Trainable Convolution Filters and Their Application to Face Recognition
—In this paper, we present a novel image classification system that is built around a core of trainable filter ensembles that we call Volterra kernel classifiers. Our system trea...
Ritwik Kumar, Arunava Banerjee, Baba C. Vemuri, Ha...