Pattern Recognition Lecture 1: Introduction and Applications
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Pattern Recognition Lecture 1: Introduction and Applications

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Questions and Answers

In pattern recognition, what is the informal definition of recognizing patterns in data?

False

In the context of medical diagnostics, what are the classes and objects?

False

What is the application for recognizing acoustic signals?

False

What application involves recognizing all possible characters, such as a, b, c,..., z?

<p>False</p> Signup and view all the answers

In the toy application of fish sorting, what is the classifier being used?

<p>False</p> Signup and view all the answers

Using fish length as the discriminating feature resulted in a classification error of 34%.

<p>True</p> Signup and view all the answers

The best length threshold (L) found for classifying fish was 12.

<p>False</p> Signup and view all the answers

When using fish lightness as the discriminating feature, the classification error was 8%.

<p>True</p> Signup and view all the answers

Combining both length and lightness features resulted in a classification error of 4%.

<p>True</p> Signup and view all the answers

A complicated decision boundary that doesn't generalize well to new data is known as underfitting.

<p>False</p> Signup and view all the answers

Pattern recognition involves assigning an object or an event to one of several pre-specified categories.

<p>True</p> Signup and view all the answers

The toy application of fish sorting involves sorting salmon and camera on a conveyer belt.

<p>False</p> Signup and view all the answers

In medical diagnostics application, the classes are tumors and cancer.

<p>False</p> Signup and view all the answers

In pattern recognition, the classes for the application of recognizing all possible characters (a, b, c,..., z) are phonemes.

<p>False</p> Signup and view all the answers

An underfitting decision boundary is known to generalize well to new data.

<p>False</p> Signup and view all the answers

The classifier performed ideally on the new data, with 0% classification error.

<p>False</p> Signup and view all the answers

Using fish length as the discriminating feature alone resulted in a classification error of 34%.

<p>True</p> Signup and view all the answers

The best length threshold (L) found for classifying fish was 9.

<p>True</p> Signup and view all the answers

Overfitting the data results in complicated boundaries that do not generalize well to new data.

<p>True</p> Signup and view all the answers

Combining both length and lightness features resulted in a classification error of 8%.

<p>False</p> Signup and view all the answers

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