Results 241 to 250 of about 693,025 (273)

A Hybrid Higher Order Neural Classifier for handling classification problems

Expert Systems with Applications, 2011
In this paper, we propose a novel Hybrid Higher Order Neural Classifier (HHONC) which contains different high-order units. In contrast with conventional fully-connected higher order neural networks (HONN), our proposed method uses fewer learning parameters and allocates the best fitted model in dealing with different datasets by modifying the orders of
Mehdi Fallahnezhad, Mohammad Hassan Moradi 0001, Salman Zaferanlouei   +2 more
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METHODS OF HANDLING AND CLASSIFYING TRADE CATALOGUES

Aslib Proceedings, 1953
On appointment as librarian, the author took over 1,500 trade catalogues in addition to the usual library stock. The catalogues had been stored on shelves, loosely inserted in Manila folders, a system which proved hopelessly inefficient. It was necessary to continue to use the shelving but, because of the varying size of the catalogues, box files would
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Pedestrian Detection for Autonomous Cars: Occlusion Handling by Classifying Body Parts

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020
In this work, we address the problem of detecting body parts of pedestrians using deep neural networks. In particular, we consider the occluded pedestrian detection problem in autonomous driving settings. While state-of-the-art deep neural models perform reasonably well for detecting full-body pedestrians, their performances are not satisfactory for ...
Muhammad Mobaidul Islam, Abdullah Al Redwan Newaz, Balakrishna Gokaraju, Ali Karimoddini   +3 more
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Improving existing cascaded face classifier by adding occlusion handling

2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, 2012
Recent face detectors used in human robot interaction are boosted cascades. These cascades can detect upright faces but are very sensible to occlusions. We propose a generic framework to handle occlusions at prediction time in a boosted cascade. The contribution is a probabilistic formulation of the cascade structure that considers the uncertainty ...
Pierre Bouges, Thierry Chateau, Christophe Blanc, Gaëlle Loosli   +3 more
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Monitoring and classifying evidence-based workload for profiling manual handling occupations

2011 IEEE International Conference on Industrial Engineering and Engineering Management, 2011
The majority of employments with occupational musculoskeletal hazards can be classified as manual handling jobs in manufacturing and the public health sector. The purpose of this study was to monitor and classify evidence-based workload of a manual handling target group of nurses using a modified Delphi with three independent consecutive surveys.
Jan Pieter Clarys, Jonathan Tresignie, Aldo Scafoglieri, Erik Cattrysse   +3 more
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Non-monotonic inference system handling knowledge allowing classified exceptions

SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218), 2002
We propose a nonmonotonic formalism for knowledge handling allowing exceptions, the Exc-Representation (ER), and its goal-directed proof procedure, the SLD-EXC resolution, for the nonmonotonic inference system, NISE. ER always provides a unique extension, which is a set of conclusions, and makes tractable the membership problem in nonmonotonic ...
Kouzou Ohara, Noboru Babaguchi, Tadahiro Kitahashi   +2 more
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Efficient handling of high-dimensional feature spaces by randomized classifier ensembles

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02, 2002
Handling massive datasets is a difficult problem not only due to prohibitively large numbers of entries but in some cases also due to the very high dimensionality of the data. Often, severe feature selection is performed to limit the number of attributes to a manageable size, which unfortunately can lead to a loss of useful information.
Aleksander Kolcz, Xiaomei Sun, Jugal K. Kalita   +2 more
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Boosting Lite – Handling Larger Datasets and Slower Base Classifiers

2007
In this paper, we examine ensemble algorithms (Boosting Lite and Ivoting) that provide accuracy approximating a single classifier, but which require significantly fewer training examples. Such algorithms allow ensemble methods to operate on very large data sets or use very slow learning algorithms.
Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer, W. Philip Kegelmeyer   +3 more
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A probabilistic multi-label classifier with missing and noisy labels handling capability

Pattern Recognition Letters, 2017
Our multi-label classifier has the capability of handling missing and noisy labels.The proposed probabilistic framework uses auxiliary random variables called experts.An expert ensemble with an overriding expert is used to specify the label.The proposed method outperforms state-of-the-art methods by a large margin.
Amirhossein Akbarnejad, Mahdieh Soleymani Baghshah   +1 more
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Weak Classifiers Performance Measure in Handling Noisy Clinical Trial Data

2016
Most research concluded that machine learning performance is better when dealing with cleaned dataset compared to dirty dataset. In this paper, we experimented three weak or base machine learning classifiers: Decision Table, Naive Bayes and k-Nearest Neighbor to see their performance on real-world, noisy and messy clinical trial dataset rather than ...
Ezzatul Akmal Kamaru-Zaman, Andrew Brass, James Weatherall, Shuzlina Abdul Rahman   +3 more
openaire   +1 more source