2,903 research outputs found
An empirical methodology for developing stockmarket trading systems using artificial neural networks
Learning Membership Functions in a Function-Based Object Recognition System
Functionality-based recognition systems recognize objects at the category
level by reasoning about how well the objects support the expected function.
Such systems naturally associate a ``measure of goodness'' or ``membership
value'' with a recognized object. This measure of goodness is the result of
combining individual measures, or membership values, from potentially many
primitive evaluations of different properties of the object's shape. A
membership function is used to compute the membership value when evaluating a
primitive of a particular physical property of an object. In previous versions
of a recognition system known as Gruff, the membership function for each of the
primitive evaluations was hand-crafted by the system designer. In this paper,
we provide a learning component for the Gruff system, called Omlet, that
automatically learns membership functions given a set of example objects
labeled with their desired category measure. The learning algorithm is
generally applicable to any problem in which low-level membership values are
combined through an and-or tree structure to give a final overall membership
value.Comment: See http://www.jair.org/ for any accompanying file
Determining The Value-at-risk In The Shadow Of The Power Law: The Case Of The SP-500 Index
In extant financial market models, including the Black-Scholes’ contruct, the dramatic events of October 1987 and August 2007 are totally unexpected, because these models are based on the assumptions of ‘independent price fluctuations’ and the existence of some ‘fixed-point equilibrium’. This paper argues that the convolution of a generalized fractional Brownian motion (into an array in frequency or time domain) and their corresponding amplitude spectra describes the surface of the attractor driving the evolution of prices. This more realistic approach shows that the SP-500 Index is characterized by a high long term Hurst exponent and hence by a ‘black noise’ with a power spectrum proportional to f-b (b > 2). In that set up, the above dramatic events are expected and their frequencies are determined. The paper also constructs an exhaustive frequency-variation relationship which can be used as practical guide to assess the ‘value at risk’.Market Collapse; Fractional Brownian Motion; Fractal Attractors; Maximum Hausdorff Dimension of Markets and Affine Profiles; Hurst Exponent; Power Spectrum Exponent; Value at Risk
Gauge theories on quantum spaces
We review the present status of gauge theories built on various quantum
space-times described by noncommutative space-times. The mathematical tools and
notions underlying their construction are given. Different formulations of
gauge theory models on Moyal spaces as well as on quantum spaces whose
coordinates form a Lie algebra are covered, with particular emphasis on some
explored quantum properties. Recent attempts aiming to include gravity dynamics
within a noncommutative framework are also considered.Comment: 141 pages. Review article. This is a preliminary versio
構造化データに対する予測手法:グラフ,順序,時系列
京都大学新制・課程博士博士(情報学)甲第23439号情博第769号新制||情||131(附属図書館)京都大学大学院情報学研究科知能情報学専攻(主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 阿久津 達也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA
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