Kernel regression methods for the prediction of wood properties of Pinus taeda using near infrared spectroscopy

Wood Science and Technology

Volumn 44, Issue 4, 2010, Pages 561-578

  Mora, Christian R ^a   Schimleck, Laurence R ^a  

^a UNIVERSITY OF GEORGIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIR-DRY DENSITY; BREAST HEIGHT; CALIBRATION MODEL; KERNEL METHODS; KERNEL REGRESSION; KERNEL REGRESSION METHOD; LEAST SQUARES SUPPORT VECTOR MACHINES; MICROFIBRIL ANGLES; NEAR INFRARED DIFFUSE REFLECTANCE SPECTRUM; NON-LINEARITY; NONLINEAR FEATURES; PARTIAL LEAST SQUARES; PINUS TAEDA; PLS MODELS; PLS REGRESSION; PREDICTIVE ABILITIES; RADIAL BASIS FUNCTIONS; RADIAL STRIPS; SIGNIFICANT FACTORS; SVM MODEL; TRACHEID COARSENESS; WALL THICKNESS;

AGRICULTURAL PRODUCTS; CALIBRATION; FORECASTING; FORESTRY; INFRARED DEVICES; NEAR INFRARED SPECTROSCOPY; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

RADIAL BASIS FUNCTION NETWORKS;

AIR DRYING; CALIBRATION; FORECASTS; FORESTRY; INFRARED RADIATION; INFRARED SPECTROSCOPY; PINUS TAEDA; REGRESSION ANALYSIS; WOOD PROPERTIES;

PINUS TAEDA;

EID: 78149465287     PISSN: 00437719     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00226-009-0299-5     Document Type: Article

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