کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6864933 1439552 2018 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
ELM based architecture for general purpose automatic weight and structure learning
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
ELM based architecture for general purpose automatic weight and structure learning
چکیده انگلیسی
Currently, neural networks deliver state of the art performance on multiple machine learning tasks, mainly because of their ability to learn features. However, the architecture of the neural network still requires problem-specific tuning and the long training times and hardware requirements remain an issue. In this work, the Multi-Scale Auto-Tuned Extreme Learning Machine (MSATELM) architecture is proposed, which does not require any manual feature crafting or architecture tuning and automatically learns structure and weights using an auto-tuned ELM as building block. It learns a simple model that achieves the required accuracy. The GPU implementation in OpenCL allows handling any number of samples while still delivering portable code and high performance. Results on MNIST, CIFAR-10 and UCI datasets demonstrate that this approach provides competitive results even though no problem-specific tuning is used.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 275, 31 January 2018, Pages 804-817
نویسندگان
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