Optimal adaptive classification of agricultural produce

1999 ◽  
Vol 22 (1) ◽  
pp. 11-27 ◽  
Author(s):  
Matti Picus ◽  
Kalman Peleg
Keyword(s):  
Adaptive Classification ◽  
Agricultural Produce

Adaptive classification of hyperspectral images using local consistency

2014 ◽  
Vol 23 (6) ◽  
pp. 063014 ◽  
Author(s):  
Xiaoyong Bian ◽  
Xiaolong Zhang ◽  
Renfeng Liu ◽  
Li Ma ◽  
Xiaowei Fu
Keyword(s):  
Hyperspectral Images ◽  
Local Consistency ◽  
Adaptive Classification

scholarly journals A sequential sampling strategy for adaptive classification of computationally expensive data

2016 ◽  
Vol 55 (4) ◽  
pp. 1425-1438 ◽  
Author(s):  
Prashant Singh ◽  
Joachim van der Herten ◽  
Dirk Deschrijver ◽  
Ivo Couckuyt ◽  
Tom Dhaene
Keyword(s):  
Sequential Sampling ◽  
Sampling Strategy ◽  
Adaptive Classification ◽  
Computationally Expensive

Adaptive classification of ensonified underwater objects

1998 ◽  
Vol 104 (3) ◽  
pp. 1842-1842
Author(s):  
David A. Helweg ◽  
Patrick W. B. Moore
Keyword(s):  
Adaptive Classification

scholarly journals Adaptive Classification of Multi-Spectral Images Using Local Uniformity

1985 ◽  
Vol 21 (2) ◽  
pp. 164-171
Author(s):  
Tetsuya TAKAHASHI ◽  
Sadao FUJIMURA ◽  
Hiromichi TOYOTA
Keyword(s):  
Adaptive Classification

Multi-Label Genre Classification of Web Pages Using an Adaptive Centroid-Based Classifier

2016 ◽  
Vol 15 (01) ◽  
pp. 1650008 ◽  
Author(s):  
Chaker Jebari
Keyword(s):  
Computational Complexity ◽  
Rapid Evolution ◽  
Classification Method ◽  
Training Dataset ◽  
Complex Object ◽  
Web Pages ◽  
Web Page ◽  
Adaptive Classification ◽  
Genre Classification

This paper proposes an adaptive centroid-based classifier (ACC) for multi-label classification of web pages. Using a set of multi-genre training dataset, ACC constructs a centroid for each genre. To deal with the rapid evolution of web genres, ACC implements an adaptive classification method where web pages are classified one by one. For each web page, ACC calculated its similarity with all genre centroids. Based on this similarity, ACC either adjusts the genre centroid by including the new web page or discards it. A web page is a complex object that contains different sections belonging to different genres. To handle this complexity, ACC implements a multi-label classification where a web page can be assigned to multiple genres at the same time. To improve the performance of genre classification, we propose to aggregate the classifications produced using character n-grams extracted from URL, title, headings and anchors. Experiments conducted using a known multi-label dataset show that ACC outperforms many other multi-label classifiers and has the lowest computational complexity.

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