REDUCED FALSE POSITIVE IDENTIFICATION FOR SPECTROSCOPIC CLASSIFICATION

A device may receive information identifying results of a set of spectroscopic measurements of a training set of known samples and a validation set of known samples. The device may generate a classification model based on the information identifying the results of the set of spectroscopic measuremen...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	HSIUNG, ChangMeng, SUN, Lan, PEDERSON, Christopher G, VON GUNTEN, Marc K
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COLORIMETRY COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT,POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED,VISIBLE OR ULTRA-VIOLET LIGHT MEASURING PHYSICS RADIATION PYROMETRY TESTING
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	A device may receive information identifying results of a set of spectroscopic measurements of a training set of known samples and a validation set of known samples. The device may generate a classification model based on the information identifying the results of the set of spectroscopic measurements, wherein the classification model includes at least one class relating to a material of interest for a spectroscopic determination, and wherein the classification model includes a no-match class relating to at least one of at least one material that is not of interest or a baseline spectroscopic measurement. The device may receive information identifying a particular result of a particular spectroscopic measurement of an unknown sample. The device may determine whether the unknown sample is included in the no-match class using the classification model. The device may provide output indicating whether the unknown sample is included in the no-match class.