April 2019
Intermediate to advanced
214 pages
5h 57m
German
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KAPITEL 6
Dimensionsreduktion: Mit dem Hauptkomponentenverfahren die Datenwolke flach drücken
Mithilfe automatischer Verfahren zur Datensammlung und Merkmalserzeugung kann man im Handumdrehen eine große Zahl von Merkmalen bekommen. Aber nicht alle davon sind sinnvoll. In den Kapiteln 3 und 4 besprachen wir das Filtern nach Häufigkeit und die Merkmalsskalierung als Möglichkeiten, um aussagelose Merkmale zu beseitigen. Nun werfen wir einen genauen Blick auf die Reduktion der Merkmalsdimensionen mithilfe des Hauptkomponentenverfahrens (PCA, engl. Principal Component Analysis).
Dieses Kapitel stellt einen Einstieg in die modellbezogenen Verfahren zur Merkmalskonstruktion dar. Die meisten bisher gezeigten Verfahren funktionieren unabhängig von den ...
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