کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6855567 660780 2016 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Comparison between Bayesian network classifiers and SVMs for semantic localization
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Comparison between Bayesian network classifiers and SVMs for semantic localization
چکیده انگلیسی
This work presents a methodology to apply Bayesian networks classifiers (BNCs) to the problem of semantic localization in robotics. This task consists of determining where the robot is located by using semantic annotations instead of metric locations, and based on robots perceptions, namely images. The proposal covers the two key steps of (1) extracting descriptive features from the input image and (2) construction and evaluation of models, comparing the performance of BNCs technologies with SVMs solutions. The experimentation is performed over two different datasets, and the results, given in terms of accuracy, provide a quite appealing analysis where specialization versus generalization or model complexity are considered. Overall BNCs proved to be quite competitive, and appear to be a very promising tool for future applications since they would allow the introduction of additional contextual information to the processing pipeline.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 64, 1 December 2016, Pages 434-443
نویسندگان
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