Optimal design of an unsupervised adaptive classifier with unknown priors

An adaptive detection scheme for M hypotheses was analyzed. It was assumed that the probability density function under each hypothesis was known, and that the prior probabilities of the M hypotheses were unknown and sequentially estimated. Each observation vector was classified using the current estimate of the prior probabilities. Using a set of nonlinear transformations, and applying stochastic approximation theory, an optimally converging adaptive detection and estimation scheme was designed. The optimality of the scheme lies in the fact that convergence to the true prior probabilities is ensured, and that the asymptotic error variance is minimum, for the class of nonlinear transformations considered. An expression for the asymptotic mean square error variance of the scheme was also obtained

The decision tree approach to classification

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers

Learning Control Systems -Review and Outlook

The basic concept of learning control is introduced. The following four learning schemes are briefly reviewed: (l) trainable controllers using linear classifiers, (2) reinforcement learning control systems, (3) Bayesian estimation, and (J-i) stocha.stic approximation. Potential replications and problems for further research in learning control are outlined

A method of supervised pattern recognition by an adaptive hypersphere decision threshold

In this study the Bayes likelihood detector is combined with an adaptive decision threshold classifier to solve the multicategory pattern recognition problem. It is assumed that the pattern classes can be represented by an n-dimensional vector sample taken from a multivariate gaussian probability distribution. This study presents (1) the derivation of the A̲daptive H̲ypersphere D̲ecision T̲hreshold classifier (AHDT classifier) and shows (2) how the AHDT classifier minimizes the probability of error using the learning patterns. Finally the AHDT classifier is applied to the solution of a physical problem through computer simulation --Abstract, page ii

On pattern classification algorithms - Introduction and survey

Pattern recognition algorithms, and mathematical techniques of estimation, decision making, and optimization theor

The ERTS-1 investigation (ER-600): A compendium of analysis results of the utility of ERTS-1 data for land resources management

The results of the ERTS-1 investigations conducted by the Earth Observations Division at the NASA Lyndon B. Johnson Space Center are summarized in this report, which is an overview of documents detailing individual investigations. Conventional image interpretation and computer-aided classification procedures were the two basic techniques used in analyzing the data for detecting, identifying, locating, and measuring surface features related to earth resources. Data from the ERTS-1 multispectral scanner system were useful for all applications studied, which included agriculture, coastal and estuarine analysis, forestry, range, land use and urban land use, and signature extension. Percentage classification accuracies are cited for the conventional and computer-aided techniques

Investigations in adaptive processing of multispectral data

Adaptive data processing procedures are applied to the problem of classifying objects in a scene scanned by multispectral sensor. These procedures show a performance improvement over standard nonadaptive techniques. Some sources of error in classification are identified and those correctable by adaptive processing are discussed. Experiments in adaptation of signature means by decision-directed methods are described. Some of these methods assume correlation between the trajectories of different signature means; for others this assumption is not made

Evaluation and comparison of ERTS measurements of major crops and soil associations for selected test sites in the central United States

The author has identified the following significant results. Multispectral scanner data obtained by ERTS-1 over six test sites in the Central United States were analyzed and interpreted. ERTS-1 data for some of the test sites were geometrically corrected and temporally overlayed. Computer-implemented pattern recognition techniques were used in the analysis of all multispectral data. These techniques were used to evaluate ERTS-1 data as a tool for soil survey. Geology maps and land use inventories were prepared by digital analysis of multispectral data. Identification and mapping of crop species and rangelands were achieved throught the analysis of 1972 and 1973 ERTS-1 data. Multiple dates of ERTS-1 data were examined to determine the variation with time of the areal extent of surface water resources on the Southern Great Plain

The ERTS-1 investigation. Volume 7: Erts-1 land-use analysis of the Houston area test site

There are no author-identified significant results in this report