Why clustering in function approximation? Theoretical explanation
نویسندگان
چکیده
منابع مشابه
Why clustering in function approximation? Theoretical explanation
Follow this and additional works at: http://digitalcommons.utep.edu/cs_techrep Part of the Computer Engineering Commons Comments: UTEP-CS-99-3. Preliminary version published by The Chinese University of Hong Kong, Department of Mechanical and Automation Engineering, as Technical Report CUHK-MAE-99-001, January 1999; full version published in International Journal of Intelligent Systems, 2000, V...
متن کاملWhy Rectified Linear Neurons Are Efficient: A Possible Theoretical Explanation
Traditionally, neural networks used a sigmoid activation function. Recently, it turned out that piecewise linear activation functions are much more efficient – especially in deep learning applications. However, so far, there have been no convincing theoretical explanation for this empirical efficiency. In this paper, we provide such an explanation. 1 Rectified Linear Neurons: Formulation of the...
متن کاملWhy Deep Neural Networks: A Possible Theoretical Explanation
In the past, the most widely used neural networks were 3layer ones. These networks were preferred, since one of the main advantages of the biological neural networks – which motivated the use of neural networks in computing – is their parallelism, and 3-layer networks provide the largest degree of parallelism. Recently, however, it was empirically shown that, in spite of this argument, multi-la...
متن کاملWhy Deep Neural Networks for Function Approximation?
Recently there has been much interest in understanding why deep neural networks are preferred to shallow networks. We show that, for a large class of piecewise smooth functions, the number of neurons needed by a shallow network to approximate a function is exponentially larger than the corresponding number of neurons needed by a deep network for a given degree of function approximation. First, ...
متن کاملA new clustering technique for function approximation
To date, clustering techniques have always been oriented to solve classification and pattern recognition problems. However, some authors have applied them unchanged to construct initial models for function approximators. Nevertheless, classification and function approximation problems present quite different objectives. Therefore it is necessary to design new clustering algorithms specialized i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Intelligent Systems
سال: 2000
ISSN: 0884-8173,1098-111X
DOI: 10.1002/1098-111x(200010)15:10<959::aid-int4>3.0.co;2-b