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- W4235231693 abstract "The model of data can be built by choice of probability distribution function and nodes combination. PFC modeling via nodes combination and parameter ? as probability distribution function enables value anticipation in risk analysis and decision making. Two-dimensional curve is extrapolated and interpolated via nodes combination and different functions as discrete or continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function. The method of Probabilistic Features Combination (PFC) enables interpolation and modeling of high-dimensional data using features' combinations and different coefficients ? as modeling function. Functions for ? calculations are chosen individually at each data modeling and it is treated as N-dimensional probability distribution function: ? depends on initial requirements and features' specifications. PFC method leads to data interpolation as handwriting or signature identification and image retrieval via discrete set of feature vectors in N-dimensional feature space." @default.
- W4235231693 created "2022-05-12" @default.
- W4235231693 date "2017-03-24" @default.
- W4235231693 modified "2023-09-27" @default.
- W4235231693 title "PNC in 3D Surface Modeling" @default.
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- W4235231693 doi "https://doi.org/10.4018/978-1-5225-2531-8.ch005" @default.
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