[Recording available] Five levels of Pattern Recognition (PARC)
Time: 15.00-16.00 (CEST)
Sartorius Stedim Data Analytics (SSDA) invites to a webinar on the Umetrics® Suite and its use in Pattern Recognition
Pattern recognition (PARC) is often described as a procedure for formulating rules of classification. Based on a set of given classes, each of which contains a number of observations mapped by a multitude of variables, guidelines and rules are developed that make it possible to classify new observations as similar or dissimilar to the members of the existing classes. PARC has been used in a wide variety of applications. In analytical chemistry, PARC is used to classify observations like mixtures of chemical compounds, or pure compounds for which the structure is to be determined. In environmental chemistry, PARC is useful for categorizing classes of partly related pesticides, in order to understand their similarity and predict important properties, like toxicity, biodegradation, and soil sorption. In food science, PARC is used extensively for classifying food products according to principles of geographic origin, textural properties, or physical and chemical properties.
Applications of pattern recognition in chemistry, biology, “omics”, engineering and other fields can be categorized into five types of problem depending on the level and scope of the study. Attend this webinar and get an introduction into the secrets of Pattern Recognition.
Topics for this webinar:
- An introduction to pattern recognition
- PARC Level 1: Classification into one of a number of specified classes
- PARC Level 2: Level 1 plus the possibility of outliers
- PARC Level 3: Level 2 plus a response variable (y)
- PARC Level 4: Level 3, but with a matrix of responses (Y)
- PARC Level 5: Level 4, but with multiple tables of data
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