IT (lectures 6& 7) Flashcards

1
Q

A …… is an abstraction, represented by a set of measurements describing a physical object

A

Pattern

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2
Q

……. is a set of patterns sharing common attributes

A

Pattern class

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3
Q

…. refers to some form of adaptation of the classification algorithm to achieve a better response

A

Learning

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4
Q

…… used to reduce the classification error on a set of training data

A

Training/ learning

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5
Q

If the classes to which the objects belong are known, the process is called ……..

A

Supervised learning

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6
Q

If the classes to which the objects belong are unknown, the process is called ……

A

Unsupervised learning

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7
Q

In ….., no labeled training sets are provided

A

Unsupervised learning

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8
Q

……. is measurements of physical variables

A

Data acquisition and sensing

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9
Q

Removal of noise in data is in ….. stage

A

Pre-processing

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10
Q

Isolation of patterns of interest from the background is in …… stage

A

Pre-processing

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11
Q

…… partitions an image into regions that are meaningful for a particular task

A

Segmentation

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12
Q

….. methods, in which similarities are detected

A

Regio-based

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13
Q

….. methods, in which discontinuities are detected and linked to form continuous boundaries around regions

A

Boundary-based

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14
Q

Finding a new representation in terms of features is in ….. stage

A

Feature extraction

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15
Q

….. are characteristic properties of the objects whose value should be similar for objects in a particular class, and different from the values for objects in another class

A

Features

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16
Q

Features should be ….., they should normally be invariant to translation, orientation (rotation), scale, and illumination

A

Robust

17
Q

Features should be ……, the range of values for objects in different classes should be different and preferably be well separated and non-overlapping

A

Discriminating

18
Q

Features should be ….., all objects of the same class should have similar values

A

Reliable

19
Q

Features should be ….., uncorrelated; as a counter-example, length and area are correlated and it would be wasteful to consider both as separate features

A

Independent

20
Q

….. extracts features from raw data

A

Feature extractor

21
Q

….. is choosing the most informative subsetbof features, and removing as many irrelevant and redundant features as possible

A

Feature selection

22
Q

…… is using features and learned models to assign a pattern to a category

A

Classification

23
Q

……. assigns objects to certain categories (or classes) based on the feature information

A

Classification

24
Q

Evaluation of confidence in decisions is in ….. stage

A

Post-processing

25
Q

K-Nearest Neighbor is an example of …..

A

Classification

26
Q

K-Means is an example of …..

A

Clustering

27
Q

…… stores all available cases and classifies new cases based on a similarity measure

A

K-Nearest Neighbor

28
Q

Lazy learning algorithm

A

K-Nearest Neighbor