The Design Cycle
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Data collection
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Feature Choice
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Model Choice
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Training
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Evaluation
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Computational Complexity
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Data Collection
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How do we know when we have collected an
adequately large and representative set of examples for training and testing
the system?
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Feature Choice
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Depends on the characteristics of the problem
domain. Simple to extract, invariant to irrelevant transformation insensitive
to noise.
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Model Choice
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Unsatisfied with the performance of our fish
classifier and want to jump to another class of model
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Training
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Use data to determine the classifier. Many
different procedures for training classifiers and choosing models
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Evaluation
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Measure the error rate (or performance and
switch from one set of features to another one
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Computational Complexity
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What is the trade-off between computational ease
and performance?
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(How an algorithm scales as a function of the
number of features, patterns or categories?)
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Supervised learning
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A teacher provides a category label or cost for
each pattern in the training set
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Unsupervised learning
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The system forms clusters or “natural groupings”
of the input patterns
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