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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Myths and legends in learning classification rules

A discussion is presented of machine learning theory on empirically learning classification rules. Six myths are proposed in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, universal learning algorithms, and interactive learning. Some of the problems raised are also addressed from a Bayesian perspective. Questions are suggested that machine learning researchers should be addressing both theoretically and experimentally.

Buntine, Wray↗
NASA NTRS1991

Learning classification trees

Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

Buntine, Wray↗
NASA NTRS1991

Bayesian classification theory

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass.

Hanson, Robin↗
NASA NTRS1990

Tracking and letter classification under dichoptic and binocular viewing conditions

Subjects were required to fly a simulated helicopter path, while also classifying letter pairs presented intermitently at 5 retinal locations. Binocular and Dichoptic conditions were compared by employing color filters. Tracking under dichoptic conditions was strongly influenced by the absence of a common optical axis. Classification performance also deteriorated and was influenced by the conditions of tracking.

Gopher, Daniel↗
NASA NTRS1991

The lower subsidiary diffuse plasma resonances and the classification of radio emissions below the plasma frequency

Using previusly published data and newly scaled ionograms from the Alouette 2 and ISIS 1 experiments, the diffuse ionospheric resonances Dn, stimulated by topside sounders Dn, are classified. The classification also includes the lower subsidiary resonances Dn(-) (n = 1, 2, 3, and 4). It is shown that the Dn(-) frequencies are related to f(Dn) and f(H) by the expression f(Dn)- = sq rt of (f(Dn)-squared - f(H)-squared).

Osherovich, Vladimir A.↗
NASA NTRS1991

Classification-based reasoning

A representation formalism for N-ary relations, quantification, and definition of concepts is described. Three types of conditions are associated with the concepts: (1) necessary and sufficient properties, (2) contingent properties, and (3) necessary properties. Also explained is how complex chains of inferences can be accomplished by representing existentially quantified sentences, and concepts denoted by restrictive relative clauses as classification hierarchies. The representation structures that make possible the inferences are explained first, followed by the reasoning algorithms that draw the inferences from the knowledge structures. All the ideas explained have been implemented and are part of the information retrieval component of a program called Snowy. An appendix contains a brief session with the program.

Gomez, Fernando↗
NASA NTRS1992

Classification of mafic clasts from mesosiderites - Implications for endogenous igneous processes

Results are presented from an analysis of 13 igneous pebbles from the Vaca Muerta, EET87500, and Bondoc mesosiderites, using electron microprobe and instrumental neutron activation techniques. These data, combined with literature data on compositions of 43 mesosiderite clasts were used to compile a classification scheme for the various types of mafic silicate clasts that occur in mesosiderites. These clasts were classified into five principal groups: (1) polygenic and monogenic cumulates (30 percent); (2) polygenic basalts (30 percent); (3) quench-textured rocks, comprising two compositional subgroups (those which resemble basaltic eucrites (5 percent), and those which resemble cumulate eucrites (2 percent)); (4) monogenic basalts (11 percent); and (5) ultramafic rocks, consisting mainly of large crystals of orthopyroxene (9 percent) or olivine (4 percent). The conditions under which these clasts were formed are discussed.

Rubin, Alan E.↗
NASA NTRS1992

High-resolution space-shuttle polarimetry for farm crop classification

A significant advance is reported in imaging the polarimetry of a terrestrial area of earth located along the Mississippi River near New Madrid, Missouri. Color imagery was obtained with twin Hasselblad cameras with mutually perpendicular polarization analyzers. Digitization of the imagery in three colors (red, green, and blue) was accomplished at the Johnson Space Center Video Digital Analysis System Laboratory, Houston, Texas. A ground resolution of 80-90 m was achieved in the high-resolution imagery. Percent polarization was superior to photometry for recognition and characterization of farm crops such as rice, milo, cotton, and soybeans and of fallow areas. Statistical analyses of the percent-polarization data permit a unique classification of crops. Atmospheric effects may be deduced. Space-shuttle window distortion and viewing angle-sun geometry must be taken into account in analyzing the data.

Egan, Walter G.↗
NASA NTRS1991

Characterization and classification of sea ice in polarimetric SAR data

A more detailed characterization of the apparent thin ice types in the multifrequency, multipolarization synthetic aperture radar (SAR) dataset acquired during a campaign for validation of the DMSP SSM/I radiometer ice products is given. The emphasis is on providing a more detailed characterization of the signatures of apparent thin ice types observed in this data and the utility of these signatures for ice type classification purposes. The statistical characteristics of these signatures and their dependence on system calibration are summarized. Implications of these observations for sea ice scattering models are briefly discussed.

Kwok, R.↗
NASA NTRS1991

Use of simulated neural networks of aerial image classification

The utility of one layer neural network in aerial image classification is examined. The network was trained with the delta rule. This method was shown to be useful as a classifier in aerial images with good resolution. It is fast, it is easy to implement, because it is distribution-free, nothing about statistical distribution of the data is needed, and it is very efficient as a boundary detector.

Medina, Frances I.↗
NASA NTRS1991

Supervised pixel classification using a feature space derived from an artificial visual system

Image segmentation involves labelling pixels according to their membership in image regions. This requires the understanding of what a region is. Using supervised pixel classification, the paper investigates how groups of pixels labelled manually according to perceived image semantics map onto the feature space created by an Artificial Visual System. Multiscale structure of regions are investigated and it is shown that pixels form clusters based on their geometric roles in the image intensity function, not by image semantics. A tentative abstract definition of a 'region' is proposed based on this behavior.

Baxter, Lisa C.↗
NASA NTRS1990

Myths and legends in learning classification rules

This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, 'universal' learning algorithms, and interactive learnings. Some of the problems raised are also addressed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally.

Buntine, Wray↗
NASA NTRS1993

General method of pattern classification using the two-domain theory

Human beings judge patterns (such as images) by complex mental processes, some of which may not be known, while computing machines extract features. By representing the human judgements with simple measurements and reducing them and the machine extracted features to a common metric space and fitting them by regression, the judgements of human experts rendered on a sample of patterns may be imposed on a pattern population to provide automatic classification.

Rorvig, Mark E.↗
NASA NTRS1993

Inferring heuristic classification hierarchies from natural language input

A methodology for inferring hierarchies representing heuristic knowledge about the check out, control, and monitoring sub-system (CCMS) of the space shuttle launch processing system from natural language input is explained. Our method identifies failures explicitly and implicitly described in natural language by domain experts and uses those descriptions to recommend classifications for inclusion in the experts' heuristic hierarchies.

Hull, Richard↗
NASA NTRS1992

Analyst variability in labeling of unsupervised classifications

Analyst variability in the labeling of unsupervised classifications is tested for Landsat 5 Thematic Mapper image products covering two test sites in southern California. The accuracy of results are tested using samples from a photo interpreted base map of the area. The significance of differences between analysts is indicated by comparing Kappa statistics derived from error matrices. Analyst variability is found to be statistically significant in most cases. Certain analysts provided consistently better results for a given study area or degree of training. This work demonstrates the potential influence of analyst bias on what would otherwise seem to be a fairly objective method and suggests that controls for this subjectivity should be factored into experimental designs.

Mcgwire, Kenneth C.↗
NASA NTRS1992

Multilayer perceptron, fuzzy sets, and classification

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Pal, Sankar K.↗