Kniha On-line Linear Discriminant Classifier Catrin Plumpton

On-line Linear Discriminant Classifier

and its Application to Delayed Labelling

Jazyk: Angličtina
Vazba: Brožovaná
Dostupnost: U nakladatele na objednávku
Odesíláme za 17-27 dnů
1 173
We present an on-line Linear Discriminant Classifier for streaming data (O-LDC). This is an adaptati...

Informace o knize

Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2009
Stránek
64
EAN
9783639175868
Enbook ID
06825874
Hmotnost
112
Rozměry
150 x 220 x 4

Kompletní popis

We present an on-line Linear Discriminant Classifier for streaming data (O-LDC). This is an adaptation of the Linear Discriminant Classifier, with the class means and the inverse covariance matrix re-calculated after each new data point. The classifier satisfies the properties of an on-line classifier; it learns from a single pass through the data, uses limited memory and processing power, and exhibits any-time learning. We compare the O-LDC with on-line versions of the Perceptron and balanced Winnow classifiers. Comparisons are carried out across a series of static data sets made up of two classes. The O-LDC shows higher accuracy and a better learning rate than its counterparts. As a second task we consider delayed labelling. We propose two strategies. The passive strategy 'waits' for the correct label of a data point before using it to update the classifier. The aggressive strategy, makes use of naďve labelling, using the predicted label of a data point to update the classifier. The strategies are compared across a series of static data sets. The final accuracy of both strategies was comparable, though the passive strategy showed a better learning pattern.

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