@article{(Open Science Index):https://publications.waset.org/pdf/10000512,
	  title     = {Influence of Parameters of Modeling and Data Distribution for Optimal Condition on Locally Weighted Projection Regression Method},
	  author    = {Farhad Asadi and  Mohammad Javad Mollakazemi and  Aref Ghafouri},
	  country	= {},
	  institution	= {},
	  abstract     = {Recent research in neural networks science and
neuroscience for modeling complex time series data and statistical
learning has focused mostly on learning from high input space and
signals. Local linear models are a strong choice for modeling local
nonlinearity in data series. Locally weighted projection regression is
a flexible and powerful algorithm for nonlinear approximation in
high dimensional signal spaces. In this paper, different learning
scenario of one and two dimensional data series with different
distributions are investigated for simulation and further noise is
inputted to data distribution for making different disordered
distribution in time series data and for evaluation of algorithm in
locality prediction of nonlinearity. Then, the performance of this
algorithm is simulated and also when the distribution of data is high
or when the number of data is less the sensitivity of this approach to
data distribution and influence of important parameter of local
validity in this algorithm with different data distribution is explained.
},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {8},
	  number    = {10},
	  year      = {2014},
	  pages     = {1800 - 1807},
	  ee        = {https://publications.waset.org/pdf/10000512},
	  url   	= {https://publications.waset.org/vol/94},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 94, 2014},
	}