In information technology, vanilla (pronounced vah-NIHL-uh ) is an adjective meaning plain or basic. The unfeatured version of a product is sometimes referred to as the vanilla version. The term is based on the fact that vanilla is the most popular or at least the most commonly served flavor of ice cream.
Archive for the 'Glossary' Category
Recall is the proportion of all documents in the collection that are relevant to a query and that are actually retrieved.
Precision is the proportion of the retrieved set of documents that is relevant to the query.
The fallout rate is the ratio between number of non relevant documents retrieved and the total number of non relevant documents in the collection.
Tags: information retrieval
I found these three degree of good formatting for visual presentation:
[1] Legibility – distinctness that makes perception easy
[2] Readability – writing (print or handwriting) that can be easily read
[3] Clarity – free from obscurity and easy to understand; the comprehensibility of clear expression
Therefore the first level belong to the perception, the send to the presentation and the third to the content.
In learning, the gradual withdrawal of adult (eg, teacher) support, as through instruction, modeling, questioning, feedback, etc., for a child’s performance across successive engagements, thus transferring more and more autonomy to the child.
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In game theory, a Schelling point (also called focal point) is a solution that people will tend to use in the absence of communication, because it seems natural, special or relevant to them. The concept has been introduced by the American economist Thomas Schelling in his book The Strategy of Conflict (1960).
Consider a simple example: two people unable to communicate with each other are each shown a panel of four squares and asked to select one; if and only if they both select the same one, they will each receive a prize. Three of the squares are blue and one is red. Assuming they each know nothing about the other player, but that they each do want to win the prize, then they will, reasonably, both choose the red square. Of course, the red square is not in a sense a better square; they could win by both choosing any square. And it is the “right” square to select only if a player can be sure that the other player has selected it; but by hypothesis neither can. It is the most salient, the most notable square, though, and lacking any other one most people will choose it, and this will in fact (often) work.
From Wikipedia
Humans establish mental models of how things work, or how they would behave in a particular situation. For example, having been a student at a university for a while, a student can establish a “mental model” of attending a university. That is, he goes to classes, talks to his classmates about how to accomplish certain homeworks, he knows how to interact with his professors, and etc. Suppose now a virtual university is being offered to students for online courses, and a website is to be constructed for the virtual university. This website should understand and respect the “mental models” of targeted students in order to avoid confusion for the students to find his way around at the virtual university.
Term Frequency-Inverse Document Frequency. A kind of DocumentVector. This scheme assigns a weight to each term (vocabulary word) in a given document. The weight increases proportional to the number of times the term occurs in the document, but is offset by a term which devalues terms common in the overall corpus.
One formula (apparently a simplification of (Salton and Buckley, ’88)) is the following. The weight of a term t in a document D is:
(# of occurrences of term t in this document D) * log((total # of documents)/(# of documents with mention of term t))
References:
Gerard Salton , Christopher Buckley, Term-weighting approaches in automatic text retrieval, Information Processing and Management: an International Journal, v.24 n.5, p.513-523, 1988
Copyright notice: the present content was taken from the following URL, the copyrights are reserved by the respective author/s.
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A stemmer is a program or algorithm which determines the morphological root of a given inflected (or, sometimes, derived) word form — generally a written word form.
A stemmer for English, for example, should identify the string “cats” (and possibly “catlike”, “catty” etc.) as based on the root “cat”, and “stemmer”, “stemming”, “stemmed” as based on “stem”.
Tags: clustering, text data mining
Found this nice osxhint on how to become a power user of iChat. Nice tricks like multi person chatting …
a cognitive attractor is a set of material and immaterial elements which potentially contribute to a given activity, and which are simultaneously present from the point of view of the agent. The force of the attractor resides in the combination of several factors: the visibility of the task, its cost, its value.
[more about it here: http://www.utc.fr/arco/publications/intellectica/affiche_numero.php?num=30 in french though 
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A meta-model is an explicit model of the constructs and rules needed to build specific models within a domain of interest. A valid meta-model is an ontology, but not all ontologies are modeled explicitly as meta-models. A meta-model can be viewed from three different perspectives:
1. as a set of building blocks and rules used to build models
2. as a model of a domain of interest, and
3. as an instance of another model.
When comparing meta-models to ontologies, we are talking about meta-models as models (perspective 2).
Note: Meta-modeling as a domain of interest can have its own ontology. For example, the CDIF Family of Standards, which contains the CDIF Meta-meta-model along with rules for modeling and extensibility and transfer format, is such an ontology. When modelers use a modeling tool to construct models, they are making a commitment to use the ontology implemented in the modeling tool. This model making ontology is usually called a meta-model, with “model making” as its domain of interest.
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People use the word ontology to mean different things, e.g. glossaries & data dictionaries, thesauri & taxonomies, schemas & data models, and formal ontologies & inference. A formal ontology is a controlled vocabulary expressed in an ontology representation language. This language has a grammar for using vocabulary terms to express something meaningful within a specified domain of interest. The grammar contains formal constraints (e.g., specifies what it means to be a well-formed statement, assertion, query, etc.) on how terms in the ontology’s controlled vocabulary can be used together.
People make commitments to use a specific controlled vocabulary or ontology for a domain of interest. Enforcement of an ontology’s grammar may be rigorous or lax. Frequently, the grammar for a “light-weight” ontology is not completely specified, i.e., it has implicit rules that are not explicitly documented.
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A thesaurus is a networked collection of controlled vocabulary terms. This means that a thesaurus uses associative relationships in addition to parent-child relationships. The expressiveness of the associative relationships in a thesaurus vary and can be as simple as “related to term” as in term A is related to term B.
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A controlled vocabulary is a list of terms that have been enumerated explicitly. This list is controlled by and is available from a controlled vocabulary registration authority. All terms in a controlled vocabulary should have an unambiguous, non-redundant definition. At a minimum, the following two rules should be enforced:
1. If the same term is commonly used to mean different concepts in different contexts, then its name is explicitly qualified to resolve this ambiguity.
2. If multiple terms are used to mean the same thing, one of the terms is identified as the preferred term in the controlled vocabulary and the other terms are listed as synonyms or aliases.
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A taxonomy is a collection of controlled vocabulary terms organized into a hierarchical structure. Each term in a taxonomy is in one or more parent-child relationships to other terms in the taxonomy. There may be different types of parent-child relationships in a taxonomy (e.g., whole-part, genus-species, type-instance), but good practice limits all parent-child relationships to a single parent to be of the same type. Some taxonomies allow poly-hierarchy, which means that a term can have multiple parents. This means that if a term appears in multiple places in a taxonomy, then it is the same term. Specifically, if a term has children in one place in a taxonomy, then it has the same children in every other place where it appears.
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