Supervised Learning - Classification Problem

A classification problem attempts to predict a discrete-valued output, e.g. benign or malignant, true or false; red, blue or yellow; blood types etc.

Classification problems can be plotted with different symbols for each class rather than a boolean dependent variable.

In a classification problem with more than one attribute (feature) determining the result, learning algorithm will try to fit a line to the graph separating the output categories.

More than two features are also possible, they’re just a bitch to draw.

Support Vector Machine (SVM)

Sometimes the best choice is to use infinite features, so algorithm has lots of attributes on which to base predictions.

Mathematical trick required to allow PC (limited memory) to deal with infinite number of features.

Support vector machines tutorial

(Source: ml-class.org)

Supervised Learning - Classification Problem

A classification problem attempts to predict a discrete-valued output, e.g. benign or malignant, true or false; red, blue or yellow; blood types etc.

Classification problems can be plotted with different symbols for each class rather than a boolean dependent variable.

In a classification problem with more than one attribute (feature) determining the result, learning algorithm will try to fit a line to the graph separating the output categories.

More than two features are also possible, they’re just a bitch to draw.

Support Vector Machine (SVM)

Sometimes the best choice is to use infinite features, so algorithm has lots of attributes on which to base predictions.

Mathematical trick required to allow PC (limited memory) to deal with infinite number of features.

Support vector machines tutorial

(Source: ml-class.org)

Posted 2 years ago & Filed under classification, discrete, supervised, ml-class, stanford, 49 notes

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