1.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: DM&KD 6(3) <1>
发信站: 南京大学小百合站 (Thu Jul 18 00:36:48 2002), 站内信件

篇名： Automated Remote Sensing with Near Infrared Reflectance Spectra&colon
; Carbonate Recognition 
刊名： Data Mining and Knowledge Discovery 
ISSN： 1384-5810 
卷期： 6 卷 3 期 出版日期： 200207  
页码： 从 277 页到 293 页共 17 页 
作者： Ramsey Joseph   Department of Philosophy, Carnegie Mellon University
, Pittsburgh, PA, USA. [e-mail]
 
Gazis Paul   NASA Ames Research Center, Mountain View, CA, USA. pgazis@mail
.arc.nasa.gov
 
Roush Ted   NASA Ames Research Center, Mountain View, CA, USA. troush@mail
.arc.nasa.gov
 
Spirtes Peter   Department of Philosophy, Carnegie Mellon University, Pittsburgh
, PA, USA&semi; Institute for Human and Machine Cognition, University of 
West Florida, USA. [e-mail]
 
Glymour Clark   Department of Philosophy and Center for Automated Learning
 and Discovery, Carnegie Mellon University, Pittsburgh, PA, USA&semi; Institute
 for Human and Machine Cognition, University of West Florida, USA. cg09@andrew
.cmu.edu
 
 
文摘： 
Reflectance spectroscopy is a standard tool for studying the mineral composition
 of rock and soil samples and for remote sensing of terrestrial and extraterr
estrial
 surfaces. We describe research on automated methods of mineral identification
 from reflectance spectra and give evidence that a simple algorithm, adapted
 from a well-known search procedure for Bayes nets, identifies the most frequ
ently
 occurring classes of carbonates with reliability equal to or greater than
 that of human experts. We compare the reliability of the procedure to the
 reliability of several other automated methods adapted to the same purpose
. Evidence is given that the procedure can be applied to some other mineral
 classes as well. Since the procedure is fast with low memory requirements
, it is suitable for on-board scientific analysis by orbiters or surface 
rovers.
 

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