This paper continues the development of a heuristic initialization methodology for designing multilayer feedforward neural networks aimed at modeling nonlinear functions for engineering mechanics applications as presented previously at IMAC XXIV and XXV. Seeking a transparent and domain knowledge-based approach for neural network initialization and result interpretation, this study examines the...

Recently, the unified inverse depth parametrization has shown to be a good option for challenging monocular SLAM problem, in a scheme of EKF for the estimation of the stochastic map and camera pose. In the original approach, features are initialized in the first frame observed (undelayed initialization), this aspect has advantages but also some problems. In this paper a delayed feature initiali...

A new multilayer preceptor initialization method is proposed and compared experimentally with a traditional random initialization method. An operator maps training-set vectors into a two-variate space, inspects bi-variate training-set vectors and controls the complexity of the decision boundary. Simulations with sixteen real-world pattern classi®cation tasks have shown that in small-scale patte...

The k-modes algorithm has become a popular technique in solving categorical data clustering problems in different application domains. However, the algorithm requires random selection of initial points for the clusters. Different initial points often lead to considerable distinct clustering results. In this paper we present an experimental study on applying a farthest-point heuristic based init...

Recent work has sparked new interest in type-supervised part-of-speech tagging, a data setting in which no labeled sentences are available, but the set of allowed tags is known for each word type. This paper describes observational initialization, a novel technique for initializing EM when training a type-supervised HMM tagger. Our initializer allocates probability mass to unambiguous transitio...

A new initialization method for hidden parameters in a neural network is proposed. Derived from the integral representation of neural networks, a nonparametric probability distribution of hidden parameters is introduced. In this proposal, hidden parameters are initialized by samples drawn from this distribution, and output parameters are fitted by ordinary linear regression. Numerical experimen...