2 research outputs found
The analytical design of spectral measurements for multispectral remote sensor systems
The author has identified the following significant results. In order to choose a design which will be optimal for the largest class of remote sensing problems, a method was developed which attempted to represent the spectral response function from a scene as accurately as possible. The performance of the overall recognition system was studied relative to the accuracy of the spectral representation. The spectral representation was only one of a set of five interrelated parameter categories which also included the spatial representation parameter, the signal to noise ratio, ancillary data, and information classes. The spectral response functions observed from a stratum were modeled as a stochastic process with a Gaussian probability measure. The criterion for spectral representation was defined by the minimum expected mean-square error
Pattern recognition with discrete and mixed data : theory and practice
This thesis is devoted to aspects related to the analysis of medical data bases in the
context of pattern recognition. It contains both theoretical aspects and practical
applications and its scope includes questions and problems that arise when
applying pattern recognition methods and techniques to this type of data. The goal of the application of statistical pattern recognition techniques to medical
records, is the classification of the (disease) patterns that may be present in such
records in terms of the information they contain. Typically, a medical record
contains a description of history, symptoms, results from laboratory tests, signals,
etc., all related to a given patient, i.e. all the information normally required by a
physician when making a diagnosis and/or a prognosis. Pattern recognition may
be used in order to obtain procedures (computer implemented algorithms) to assign
diagnostic or prognostic classes to a given patient, on the basis of information also
used by a physician. These procedures are not intended to replace but to assist the
physician in the decision making process. The procedures are called classifiers or
discriminants and the symptoms, signals, etc., are called features. Each individual
record is termed an object, and a collection of objects with qualitatively and/or
quantitatively similar characteristics, as established by an expert, is called a class.
It should be clear that pattern recognition can be applied to a wide variety of areas
and problems, of which (computer-aided) medical decision making is just an
example.
In order to arrive at a classifier and restricting ourselves to what is called
supervised learning, a set of objects known a-priori to belong to two or more
classes (depending on the problem at hand) is needed. In this set, each object must
be represented by a group of features and have a class assigned to it. The role of
medical data bases is now clear: they are the set of objects required for supervised
learning