Modify the training set of Fig. 12.6 so that example b also includes the word “n

Question

Modify the training set of Fig. 12.6 so that example b also includes the word “nigeria” (yet remains a negative example – perhaps someone telling about their trip to Nigeria). Find a weight vector th at separates the positive and negative examples, using: (a) The basic training method of Section 12.2.1. (b) The Winnow method of Section 12.2.3.

Example 12.6:

Let us modify the training set of Fig. 12.6 to incorporate a sixth “word” that represents the negative of the threshold. The new data is shown in Fig. 12.8.

http://infolab.stanford.edu/~ullman/mmds/book.pdf

and viagra the of nigeria y a 1 1 0 1 1 -1 +1 b 0 0 1 1 0 -1 -1 c 0 1 1 0 0 -1 +1 d 1 0 0 1 0 -1 -1 e 1 0 1 0 1 -1 +1 f 1 0 1 1 0 -1 -1

Explanation / Answer

Classification is one of the most important operators that is used for phenomenal (or similarity) searches in various image, video, and data mining applications. In a phenomenal search, a target pattern is usually classified according to a set of predefined classes. The target pattern can include, for instance, the spectral signature of a pixel from an image or video frame; the spatial signature of a block of an image or video frame defined by its texture features; the frequency signature of a time series such as stock index movement; or the spatial signature of 3D seismic data.

In order to achieve high classification accuracy, it is usually necessary to train a classifier with sufficient training data from each individual class. However, gathering reliable training data is usually difficult, if even feasible. As an example, the current United States land cover/land use maps were developed around the late 1960's by the United States Geology Survey (USGS). These maps are not completely accurate due to errors in the photointerpretation of the images used to create them, their limited resolution and inaccuracies in geolocation. Additional errors arise when using these maps as source of ground truth in conjunction to more recent images to train the classifier, due to various natural and artificial land cover transformation. As a result, the accuracy of the classifier suffers.

Similarly, classifying video, time series, and 3D seismic data could also encounter unreliable training data.

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