This article addresses the ability of Parallel Distributed Processing (PDP) networks to generate stagewise cognitive development in accordance with Piaget’s theory of cognitive epigenesis. We carried out a replication study of the simulation experiments by McClelland (1989) and McClelland and Jenkins (1991) in which a PDP network learns to solve balance scale problems. In objective tests motiva...

A stagewise theoretical substantiation of the renovation vibrowave method influencing near-wellbore zone reservoir for restoring well productivity is presented. The area treatment by proposed covers with a heterogeneous permeability fractures formed fracking. In this decrease in concentration colmatants occurs due to change direction contaminants migration. Under influence pressure pulses, they...

Lysimetric and eddy covariance techniques are commonly used to directly estimate actual crop evapotranspiration (ETa). However, these technologies costly, laborious, require skills which make in situ ET estimation difficult, particularly developing countries. With this mind, an attempt was made determine ETa stagewise coefficient (Kc) values of transplanted puddled rice using a modified non-wei...

Boosting methods are highly popular and effective supervised learning methods which combine weak learners into a single accurate model with good statistical performance. In this paper, we analyze two well-known boosting methods, AdaBoost and Incremental Forward Stagewise Regression (FSε), by establishing their precise connections to the Mirror Descent algorithm, which is a first-order method in...

We propose a new basic trapdoor `IC (`-Invertible Cycles) of the mixed field type forMultivariate Quadratic public key cryptosystems. This is the first new basic trapdoor since the invention of Unbalanced Oil and Vinegar in 1997. `IC can be considered an extended form of the well-known Matsumoto-Imai Scheme A (also MIA or C∗), and share some features of stagewise triangular systems. However `IC...

Hard margin support vector machines (HM-SVMs) have a risk of getting overfitting in the presence of the noise. Soft margin SVMs deal with this problem by the introduction of the capacity control term and obtain the state of the art performance. However, this disposal leads to a relatively high computational cost. In this paper, an alternative method, greedy stagewise algorithm, named GS-SVMs is...