We present a method to decrease the storage and communication complexity of the context-tree weighting method. This method is based on combining the estimated probability of a node in the context tree and weighted probabilities of its children in one single variable. This variable is represented by its logarithm.

Optimizing weighting factors for a linear combination of terms in a scoring function is a crucial step for success in developing a threading algorithm. Usually weighting factors are optimized to yield the highest success rate on a training dataset, and the determined constant values for the weighting factors are used for any target sequence. Here we explore completely different approaches to ha...

In this paper the novel method of “weighted OFDM” is addressed. Different types of weighting factors (including Rectangular, Bartlett, Gaussian, Raised cosine, Half-sin and Shanon) are considered. The impact of weighting of OFDM on the peak-to-average power ratio (PAPR) is investigated by means of simulation and is compared for the above mentioned weighting factors. Results show that by weighti...

An extension to the theory of consensus values is presented. Consensus values are calculated from averages obtained from different sources of measurement. Each source may have its own variability. For each average a weighting factor is calculated, consisting of contributions from both the within- and the between-source variability. An iteration procedure is used and calculational details are pr...

A simple linear averaging of the outputs of several networks as e.g. in bagging 3], seems to follow naturally from a bias/variance decomposition of the sum-squared error. The sum-squared error of the average model is a quadratic function of the weighting factors assigned to the networks in the ensemble 7], suggesting a quadratic programmingalgorithm for nding the \optimal"weighting factors. If ...

We describe a sequential universal data compression procedure for binary tree sources that performs the " double mixture. " Using a context tree, this method weights in an efficient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desirable coding distribution for tree sources with an unknown model and unknown parameters. Computational and ...

In this paper we describe an approach to the problem of weighting various factors that contribute to an analysis or outcome of a problem situation and discuss issues about weighting as they touch upon our case-based reasoned HY@O. In HYPO, we take the approach of delaying for as long as possible any assignment of weights-and of symbolically comparing the “weights” of competing factors. We call ...

Predictive torque control (PTC) is a promising method for electric machines due to its simplicity, fast dynamics, ability handle nonlinearities, and easy inclusion of additional objectives. The main challenge in conventional PTC design determine the weighting factors cost function. These are generally chosen by trial-and-error or metaheuristic optimization algorithms, but these methods may not ...