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About 1,508 papers for “weak classifiers”

Combinations of Weak Classifiers

Chuanyi Ji, et al. · 1997

To obtain classification systems with both good generalizatìon performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They are then combined through a majority vote. As demonstrated through systematic experiments, the method developed is able to obtain combinations of weak classifiers with good generalization performance and a fast training time on a variety of test problems and real applications. Theoretical analysis on one of the test problems investigated in our experiments provides insights on when and why the proposed method works. In particular, when the strength of weak classifiers is properly chosen, combinations of weak classifiers can achieve a good generalization performance with polynomial space- and time-complexity.

Cited by 0

Combinations of weak classifiers

Chuanyi Ji, Sheng Ma · IEEE Transactions on Neural Networks · 1997

To obtain classification systems with both good generalization performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They are then combined through a majority vote. As demonstrated through systematic experiments, the method developed is able to obtain combinations of weak classifiers with good generalization performance and a fast training time on a variety of test problems and real applications. Theoretical analysis on one of the test problems investigated in our experiments provides insights on when and why the proposed method works. In particular, when the strength of weak classifiers is properly chosen, combinations of weak classifiers can achieve a good generalization performance with polynomial space- and time-complexity.

Cited by 4

Majority vote based on weak classifiers

Dechang Chen, Xiuzhen Cheng · 2000

We present a two-class pattern recognition method through the majority vote which is based on weak classifiers. The weak classifiers are defined in terms of rectangular regions formed by the original training data. Tests on real and simulated data sets show that this classifier combination procedure can lead to a high accuracy.

Cited by 3

Neural weak classifiers for language learning

Michael Towsey, Claire D’Este, Joachim Diederich · 2000

We describe a neural implementation of the combination of weak classifiers (CWC) algorithm [Ji and Ma, 1997] which is able to learn the one-step-look-ahead task where the input is natural language sentences. The one-step-look-ahead task is more usually implemented with Simple Recurrent Networks [Elman, 1990] whose architecture typically consists of comparitively few neurons but learning requires many thousands of presentations of the training data. Our implementation includes a four layered architecture which consists of (1) an input layer having one neuron for each category, (2) an internally recurrent layer which captures the dynamics of the temporal input, (3) a large hidden layer of weakly classifying perceptrons and (4) a winner-take-all output layer having the same number of neurons as the input.

Cited by 0

Lightweight Active Object Retrieval with Weak Classifiers

László Czúni, Metwally Rashad · Sensors · 2018

In the last few years, there has been a steadily growing interest in autonomous vehicles and robotic systems. While many of these agents are expected to have limited resources, these systems should be able to dynamically interact with other objects in their environment. We present an approach where lightweight sensory and processing techniques, requiring very limited memory and processing power, can be successfully applied to the task of object retrieval using sensors of different modalities. We use the Hough framework to fuse optical and orientation information of the different views of the objects. In the presented spatio-temporal perception technique, we apply active vision, where, based on the analysis of initial measurements, the direction of the next view is determined to increase the hit-rate of retrieval. The performance of the proposed methods is shown on three datasets loaded with heavy noise.

Cited by 5Open access

Improved Boosting Algorithm Using Combined Weak Classifiers

Yuke Fang, Yan Fu, Chongjing Sun et al. · 2011

From family of corrective boosting algorithms (i.e. AdaBoost, LogitBoost) to total corrective algorithms (i.e. LPBoost, TotalBoost, SoftBoost, ERLPBoost), we analysis these methods of sample weight updating. Corrective boosting algorithms update the sample weight according to the last hypothesis; comparatively, total corrective algorithms update the weight with the best one of all weak classifiers. However, all these algorithms just use the local information for updating the sample weight ignoring the global information. In light of this context, we show that updating the sample weight using global information of combined weak classifiers maybe accelerate the convergence speed of boosting algorithm. By simply adding the strong classifier to the linear constraints of LPBoost, a new algorithm was proposed. The experimental results show that our algorithm can achieve both better performance and less generalization error compared to some representative boosting algorithm.

Cited by 4

On the importance of accurate weak classifier learning for boosted weak classifiers

Gary Overett, Lars Petersson · 2008

Recent work has shown that improving model learning for weak classifiers can yield significant gains in the overall accuracy of a boosted classifier. However, most published classifier boosting research relies only on rudimentary learning techniques for weak classifiers. So while it is known that improving the model learning can greatly improve the accuracy of the resulting strong classifier, it remains to be shown how much can yet be gained by further improving the model learning at the weak classifier level. This paper derives a very accurate model learning method for weak classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks using classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method.

Cited by 6

Quantaloid-enriched categories: Factorization, weak classifiers, and symmetry

García, Javier Gutiérrez, Höhle, Ulrich · arXiv (Cornell University) · 2025

This paper provides a comprehensive overview of some of the foundational properties of categories enriched over quantaloids, along with several new results. We demonstrate that the category whose objects are quantaloid-enriched categories and whose morphisms are left adjoint distributors admits an (epi, extremal mono)--factorization system. Furthermore, we prove that the category of cocomplete quantaloid-enriched categories satisfies the weak subobject classifier axiom, under stability conditions on the underlying quantaloid. As an application, we discuss how these structural results extend to quantale-valued sets, thereby generalizing the classical theory of $Ω$-valued sets.

Cited by 0Open access

Quantaloid-enriched categories: Factorization, weak classifiers, and symmetry

García, Javier Gutiérrez, Höhle, Ulrich · ArXiv.org · 2025

This paper provides a comprehensive overview of some of the foundational properties of categories enriched over quantaloids, along with several new results. We demonstrate that the category whose objects are quantaloid-enriched categories and whose morphisms are left adjoint distributors admits an (epi, extremal mono)--factorization system. Furthermore, we prove that the category of cocomplete quantaloid-enriched categories satisfies the weak subobject classifier axiom, under stability conditions on the underlying quantaloid. As an application, we discuss how these structural results extend to quantale-valued sets, thereby generalizing the classical theory of $Ω$-valued sets.

Cited by 0Open access

L.: On the importance of accurate weak classifier learning for boosted weak classifiers

Gary Overett, Lars Petersson · 2016

Abstract — Recent work [1], has shown that improving model learning for weak classifiers can yield significant gains in the overall accuracy of a boosted classifier. However, most published classifier boosting research relies only on rudimentary learning techniques for weak classifiers. So while it is known that im-proving the model learning can greatly improve the accuracy of the resulting strong classifier, it remains to be shown how much can yet be gained by further improving the model learning at the weak classifier level. This paper derives a very accurate model learning method for weak classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks us-ing classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method. I.

Cited by 0

Improved Voice Activity Detector Based on Stacking Weak Classifiers

Anton Stefanidi, Andrey Priorov, Artem Topnikov et al. · 2022

The article considers the problem of speech fragments extraction. The authors have brought together a dataset of speech signals VADSpeakersDB. This dataset consists of phonograms recorded with the help of an application for video conferences. The research considers an original algorithm based on the stacking of independent speech activity detectors and compares it with traditional approaches. The solution has a high accuracy of detecting voice fragments-more than 90%.

Cited by 0

Proceedings of ICSP2000 Majority Vote Based on Weak Classifiers

Dechang Chen, Xiuzhen Cheng · 2013

We present a two-class pattern recognition method through the majority vote which is based on weak classifiers. The weak classifiers are defined‘in terms of rectangular regions formed by the original training data. Tests on real and simulated data sets show that this classifier combination procedure can lead to a high accuracy. Key words: classification, combining, training set, test set

Cited by 0