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At least 451 records · Page 25

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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

Introduction in IND and recursive partitioning

This manual describes the IND package for learning tree classifiers from data. The package is an integrated C and C shell re-implementation of tree learning routines such as CART, C4, and various MDL and Bayesian variations. The package includes routines for experiment control, interactive operation, and analysis of tree building. The manual introduces the system and its many options, gives a basic review of tree learning, contains a guide to the literature and a glossary, and lists the manual pages for the routines and instructions on installation.

Buntine, Wray↗

NASA NTRS1991

Two papers on feed-forward networks

Connectionist feed-forward networks, trained with back-propagation, can be used both for nonlinear regression and for (discrete one-of-C) classification, depending on the form of training. This report contains two papers on feed-forward networks. The papers can be read independently. They are intended for the theoretically-aware practitioner or algorithm-designer; however, they also contain a review and comparison of several learning theories so they provide a perspective for the theoretician. The first paper works through Bayesian methods to complement back-propagation in the training of feed-forward networks. The second paper addresses a problem raised by the first: how to efficiently calculate second derivatives on feed-forward networks.

Buntine, Wray L.↗

NASA NTRS1992

Estimating the probability of failure when testing reveals no failures

Formulas for estimating the probability of failure when testing reveals no errors are introduced. These formulas incorporate random testing results, information about the input distribution, and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.

Miller, Keith W.↗

NASA NTRS1993

Classification Of Terrain In Polarimetric SAR Images

Two algorithms processing polarimetric synthetic-aperture-radar data found effective in assigning various parts of SAR images to classes representing different types of terrain. Partially automate interpretation of SAR imagery, reducing amount of photointerpretation needed and putting whole interpretation process on more quantitative and systematic basis. First algorithm implements Bayesian classification scheme "supervised" by use of training data. Second algorithm implements classification procedure unsupervised.

Van Zyl, Jakob J.↗

NASA NTRS1991

Introduction to IND and recursive partitioning, version 1.0

This manual describes the IND package for learning tree classifiers from data. The package is an integrated C and C shell re-implementation of tree learning routines such as CART, C4, and various MDL and Bayesian variations. The package includes routines for experiment control, interactive operation, and analysis of tree building. The manual introduces the system and its many options, gives a basic review of tree learning, contains a guide to the literature and a glossary, lists the manual pages for the routines, and instructions on installation.

Buntine, Wray↗

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 NTRS1992

Reliability evaluation methodology for NASA applications

Liquid rocket engine technology has been characterized by the development of complex systems containing large number of subsystems, components, and parts. The trend to even larger and more complex system is continuing. The liquid rocket engineers have been focusing mainly on performance driven designs to increase payload delivery of a launch vehicle for a given mission. In otherwords, although the failure of a single inexpensive part or component may cause the failure of the system, reliability in general has not been considered as one of the system parameters like cost or performance. Up till now, quantification of reliability has not been a consideration during system design and development in the liquid rocket industry. Engineers and managers have long been aware of the fact that the reliability of the system increases during development, but no serious attempts have been made to quantify reliability. As a result, a method to quantify reliability during design and development is needed. This includes application of probabilistic models which utilize both engineering analysis and test data. Classical methods require the use of operating data for reliability demonstration. In contrast, the method described in this paper is based on similarity, analysis, and testing combined with Bayesian statistical analysis.

Taneja, Vidya S.↗

NASA NTRS1992

Experiments with a decision-theoretic scheduler

This paper describes DTS, a decision-theoretic scheduler designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver, introduced a similar approach to solving single-agent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

Hansson, Othar↗

NASA NTRS1992

Hurricane risk assessment to rollback or ride out a cost versus loss decision making approach

The potential exists that a hurricane striking the Kennedy Space Center while a Space Shuttle is on the pad. Winds in excess of 74.5 knots could cause the failure of the holddown bolts bringing about the catastrophic loss of the entire vehicle. Current plans call for the rollback of the shuttle when winds of that magnitude are forecast to strike the center. As this is costly, a new objective method for making rollback/rideout decisions based upon Bayesian Analysis and economic cost versus loss is presented.

Wohlman, Richard A.↗

NASA NTRS1992

Signal processing for airborne doppler radar detection of hazardous wind shear as applied to NASA 1991 radar flight experiment data

Radar data collected during the 1991 NASA flight tests have been selectively analyzed to support research directed at developing both improved as well as new algorithms for detecting hazardous low-altitude windshear. Analysis of aircraft attitude data from several flights indicated that platform stability bandwidths were small compared to the data rate bandwidths which should support an assumption that radar returns can be treated as short time stationary. Various approaches at detection of weather returns in the presence of ground clutter are being investigated. Non-coventional clutter rejection through spectrum mode tracking and classification algorithms is a subject of continuing research. Based upon autoregressive modeling of the radar return time sequence, this approach may offer an alternative to overcome errors in conventional pulse-pair estimates. Adaptive filtering is being evaluated as a means of rejecting clutter with emphasis on low signal-to-clutter ratio situations, particularly in the presence of discrete clutter interference. An analysis of out-of-range clutter returns is included to illustrate effects of ground clutter interference due to range aliasing for aircraft on final approach. Data are presented to indicate how aircraft groundspeed might be corrected from the radar data as well as point to an observed problem of groundspeed estimate bias variation with radar antenna scan angle. A description of how recorded clutter return data are mixed with simulated weather returns is included. This enables the researcher to run controlled experiments to test signal processing algorithms. In the summary research efforts involving improved modelling of radar ground clutter returns and a Bayesian approach at hazard factor estimation are mentioned.

Baxa, Ernest G., Jr.↗

NASA NTRS1993

Tree Classification Software

This paper introduces the IND Tree Package to prospective users. IND does supervised learning using classification trees. This learning task is a basic tool used in the development of diagnosis, monitoring and expert systems. The IND Tree Package was developed as part of a NASA project to semi-automate the development of data analysis and modelling algorithms using artificial intelligence techniques. The IND Tree Package integrates features from CART and C4 with newer Bayesian and minimum encoding methods for growing classification trees and graphs. The IND Tree Package also provides an experimental control suite on top. The newer features give improved probability estimates often required in diagnostic and screening tasks. The package comes with a manual, Unix 'man' entries, and a guide to tree methods and research. The IND Tree Package is implemented in C under Unix and was beta-tested at university and commercial research laboratories in the United States.

Buntine, Wray↗

NASA NTRS1993

Decision-theoretic control of EUVE telescope scheduling

This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

Hansson, Othar↗

NASA NTRS1993

Decision theory for computing variable and value ordering decisions for scheduling problems

Heuristics that guide search are critical when solving large planning and scheduling problems, but most variable and value ordering heuristics are sensitive to only one feature of the search state. One wants to combine evidence from all features of the search state into a subjective probability that a value choice is best, but there has been no solid semantics for merging evidence when it is conceived in these terms. Instead, variable and value ordering decisions should be viewed as problems in decision theory. This led to two key insights: (1) The fundamental concept that allows heuristic evidence to be merged is the net incremental utility that will be achieved by assigning a value to a variable. Probability distributions about net incremental utility can merge evidence from the utility function, binary constraints, resource constraints, and other problem features. The subjective probability that a value is the best choice is then derived from probability distributions about net incremental utility. (2) The methods used for rumor control in Bayesian Networks are the primary way to prevent cycling in the computation of probable net incremental utility. These insights lead to semantically justifiable ways to compute heuristic variable and value ordering decisions that merge evidence from all available features of the search state.

Linden, Theodore A.↗

NASA NTRS1992

High-resolution gravity model of Venus

The anomalous gravity field of Venus shows high correlation with surface features revealed by radar. We extract gravity models from the Doppler tracking data from the Pioneer Venus Orbiter by means of a two-step process. In the first step, we solve the nonlinear spacecraft state estimation problem using a Kalman filter-smoother. The Kalman filter has been evaluated through simulations. This evaluation and some unusual features of the filter are discussed. In the second step, we perform a geophysical inversion using a linear Bayesian estimator. To allow an unbiased comparison between gravity and topography, we use a simulation technique to smooth and distort the radar topographic data so as to yield maps having the same characteristics as our gravity maps. The maps presented cover 2/3 of the surface of Venus and display the strong topography-gravity correlation previously reported. The topography-gravity scatter plots show two distinct trends.

Reasenberg, R. D.↗

NASA NTRS1992

A new method for the detection of a periodic signal of unknown shape and period

A method was developed for the detection and measurement of a periodic signal with unknown characteristics in a data set where the existence of such signal was not known. The method detects a signal by using Bayesian probability theory to compare a constant model for the signal to members of a class of models with periodic structure. The method was applied to simulated data generated with both stepwise and sinusoidal light curves, demonstrating that such signals can be sensitively detected and the signal frequency and its shape can be accurately estimated.

Gregory, P. C.↗

NASA NTRS1992

Estimating high mosquito-producing rice fields using spectral and spatial data

The cultivation of irrigated rice provides ideal larval habitat for a number of anopheline vectors of malaria throughout the world. Anopheles freeborni, a potential vector of human malaria, is associated with the nearly 240,000 hectares of irrigated rice grown annually in Northern and Central California; therefore, this species can serve as a model for the study of rice field anopheline population dynamics. Analysis of field data revealed that rice fields with early season canopy development, that are located near bloodmeal sources (i.e., pastures with livestock) were more likely to produce anopheline larvae than fields with less developed canopies located further from pastures. Remote sensing reflectance measurements of early-season canopy development and geographic information system (GIS) measurements of distanes between rice fields and pastures with livestock were combined to distinguish between high and low mosquito-producing rice fields. Using spectral and distance measures in either a discriminant or Bayesian analysis, the identification of high mosquito-producing fields was made with 85 percent accuracy nearly two months before anopheline larval populations peaked. Since omission errors were also minimized by these approaches, they could provide a new basis for directing abatement techniques for the control of malaria vectors.

Wood, B. L.↗

NASA NTRS1992

Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

Schenker, Paul S.↗