Low Complexity Algorithmic Trading by Feedforward Neural Networks
نویسندگان
چکیده
منابع مشابه
Circuit Complexity and Feedforward Neural Networks
Circuit complexity, a subfield of computational complexity theory, can be used to analyze how the resource usage of neural networks scales with problem size. The computational complexity of discrete feedforward neural networks is surveyed, with a comparison of classical circuits to circuits constructed from gates that compute weighted majority functions.
متن کاملPlacing Feedforward Neural Networks Among Several Circuit Complexity Classes
This paper examines the circuit complexity of feedforward neural networks having sigmoid activation function. The starting point is the complexity class NN defined in [18]. First two additional complexity classes NN∆ k and NN∆,ε k having less restrictive conditions (than NN) concerning fan-in and accuracy are defined. We then prove several relations among these three classes and well establishe...
متن کاملClasses of feedforward neural networks and their circuit complexity
-Th& paper aims to p&ce neural networks in the conte.\t ol'booh'an citz'ldt complexit.l: 1,1~, de/itte aplm~priate classes qlfeedybrward neural networks with specified fan-in, accm'ac)' olcomputation and depth and ttsing techniques" o./commzmication comph:¥ity proceed to show t/tat the classes.fit into a well-studied hieralz'h)' q/boolean circuits. Results cover both classes of sigmoid activati...
متن کاملc - Entropy and the Complexity of Feedforward Neural Networks
We develop a. new feedforward neuralnet.work represent.ation of Lipschitz functions from [0, p]n into [0,1] ba'3ed on the level sets of the function. We show that ~~ + ~€r + ( 1 + h) (:~) n is an upper bound on the number of nodes needed to represent f to within uniform error Cr, where L is the Lipschitz constant. \Ve also show that the number of bits needed to represent the weights in the netw...
متن کاملThe computational power and complexity of discrete feedforward neural networks
The number of binary functions that can be defined on a set of L vectors in N R equals 2 . For L > N the total number of threshold functions in N-dimensional space grows polynomially , while the total number of Boolean functions, definable on N binary inputs, grows exponentially ( ), and as N increases a percentage of threshold functions in relation to the total number of Boolean functions – go...
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: Computational Economics
سال: 2017
ISSN: 0927-7099,1572-9974
DOI: 10.1007/s10614-017-9720-6