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This page last changed on Aug 01, 2007 by lauvil@uiuc.edu.
The suggested Use Case Actor-Goal lists includes the following items. But if we want to add or augment the existing table, lets do so.
Actor is one of the roles we define. We probably need to define the Actors first.
Task-level Goal is a label used for this goal.
Priority is a level from 1-4 for milestone deliverables; Impact and Importance can help set this.
Impact indicates the number of people this goal satisfies.
Importance indicates how important this goal is.
Brief is a short description of this goal.
| Actor |
Task-level Goal
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Priority
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Impact |
Importance |
Brief |
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Classification:NB (Naive Bayesian)
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Simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions. Operates on the assumption that the various classifying features are independent of each other. |
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Classification:SVM (Support Vector Machine)
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Map input vectors to a higher dimensional space where a maximal separating hyperplane is constructed. Two parallel hyperplanes are constructed on each side of the hyperplane that separates the data. The separating hyperplane is the hyperplane that maximizes the distance between the two parallel hyperplanes. An assumption is made that the larger the margin or distance between these parallel hyperplanes the better the generalisation error of the classifier will be. |
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Basic Statistics
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Offers "descriptive statistics" about a text or texts of choice. |
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Clustering |
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Partitioning of a data set into subsets, so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure. |
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Frequent Pattern Analysis
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Essentially frequent pattern analysis compares every item to every other item. Algorithms have been optimized to cutoff this comparison when certain conditions are met. These frequent patterns can also be seen as an automated hypothesis generation. |
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Clustering of Frequent Pattern Analysis
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Since frequent pattern analysis generates so many rules, we can use a clustering approach to cluster similar patterns together. Given K clusters, patterns that have a common set of items are clustered together. |
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Information Extraction
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Type of information retrievalwhose goal is to automatically extract structured information, i.e. entities, categorized and contextually and semantically well-defined data from a certain domain, from unstructured machine-readable documents. |
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Select features:Based on tei xml codes
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Search:Simple |
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Search:By proximity or Boolean criteria
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Search:Basket of Words
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