In the spring of 1916, Prof. H.D. Hughes, of the Iowa Experiment Station, asked a number of experienced corn judges to score some five hundred ears of corn on the basis of what they thought the relative yields would be. These five hundred ears of corn were field run, varying from only three or four inches in length to more than ten inches. The variety was the college strain of Reid. In addition...

The k-nearest neighbors (knn) is a simple but effective method of classification. In this paper we present an extended version of this technique for chemical compounds used in High Throughput Screening, where the distances of the nearest neighbors can be taken into account. Our algorithm uses kernel weight functions as guidance for the process of defining activity in screening data. Proposed ke...

the objective of the present field experiment was to examine how plant density and enhanced source, i.e. thinning treatments, may affect grain yield and its components for a non-prolific maize hybrid, sc704. the environment of the hybrid maize crop was changed at intervals over its life cycle by removing every other plant in rows equispaced at 75 cm apart with a population of 13.34 plants m...

The optimization problems associated with various regularization techniques for supervised learning from data (e.g., weight-decay and Tikhonov regularization) are described in the context of Reproducing Kernel Hilbert Spaces. Suboptimal solutions expressed by sparse kernel models with a given upper bound on the number of kernel computational units are investigated. Improvements of some estimate...

Spike-Timing Dependent Plasticity (STDP) is characterized by a wide range of temporal kernels. However, much of the theoretical work has focused on a specific kernel - the "temporally asymmetric Hebbian" learning rules. Previous studies linked excitatory STDP to positive feedback that can account for the emergence of response selectivity. Inhibitory plasticity was associated with negative feedb...

Generalized linear models (Wedderburn and NeIder 1972, McCullagh and NeIder 1988) were introduced as a means of extending the techniques of ordinary parametric regression to several commonly-used regression models arising from non-normal likelihoods. Typically these models have a variance that depends on the mean function. However, in many cases the likelihood is unknown, but the relationship b...

This paper presents a sequential estimation procedure for an unknown regression function. Observed regressors and noises of the model are supposed to be dependent and form sequences of dependent numbers. Two types of estimators are considered. Both estimators are constructed on the basis of Nadaraya–Watson kernel estimators. First, sequential estimators with given bias and mean square error are...