We modify the definition of l-homology and argue why our definition is more adequate than the classical one. While we cannot reconstruct the classical l-homology from the new definition for various reasons, we can reconstruct its Hausdorffification so that no information concerning semi-norms is lost. We obtain an axiomatic characterization of our l-homology as a universal δ-functor and prove t...

In t h i s paper we show how t h e p a t t e r n samples g e n e r a t e d f r o m t h e unknown asymmet r i c f i n i t e m i x t u r e d i s t r i b u t i o n a re d i c h o t o m i z e d b y t h e Karhunen-Loeve o r t h o g o n a l sys tem i n t h e o p t i m a l way. We i n t r o d u c e t h e concep t o f t h e d i f f e r e n c e o f f e a t u r e s o f two c a t e g o r i e s and p r o v...

This paper describes the development of a failure diagnosis technique for V-belts through vibration monitoring. The V-belt vibration is monitored at a driven bearing body attached to a power transmission device. Seven basic causes of belt failure and their combinations are considered. Power spectra of the vibration data are calculated through noise reduction by a cross-spectrum method. Six para...

A two-factor experiment with interaction between factors wherein observations follow an Inverse Gaussian model is considered. Analysis of the experiment is approached via an empirical Bayes procedure. The conjugate family of prior distributions is considered. Bayes and empirical Bayes estimators are derived. Application of the procedure is illustrated on a data set, which has previously been an...

Naive Bayes is a well-known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The Iterative Bayes begins with the distribution tables built by the naive Bayes. Those tables are iteratively updated in order to improve the probability...