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An Intuitive Representation of ContextFree Languages
"... this paper, the following' conception of language is used. A lguage is an ordered triple L  (, C, f where an C are two disjoint, nonempty, finite sets and f is sn application of C into the set of all subsets of the free semigroup F() generated by . The set V is cslled the vocabulary (in Chom ..."
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this paper, the following' conception of language is used. A lguage is an ordered triple L  (, C, f where an C are two disjoint, nonempty, finite sets and f is sn application of C into the set of all subsets of the free semigroup F() generated by . The set V is cslled the vocabulary (in Chomsky [13 , terminal vocabu lary), its elements are called wors, those of F(V) word strings. The elements of C (which corresponds to the auxiliary vocabulary in Chom sky [1]) are called smmatioal) oateories. For any category o C, the elements of f ) F) are called the word strings belonging to the cateor,y c
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixtureof ..."
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Cited by 705 (15 self)
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We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a MixtureofGaussians model (for multimodal distributions). These probability densities are then used to formulate a maximumlikelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects such as hands.
Inductive Learning Algorithms and Representations for Text Categorization
, 1998
"... Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categori ..."
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Cited by 641 (8 self)
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categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size, and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs
Good News and Bad News: Representation Theorems and Applications
 Bell Journal of Economics
"... prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtai ..."
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Cited by 684 (3 self)
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prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at
and W.Hsu, “Intuitive Representation of Decision Trees Using General Rules and Exceptions
"... Producing too many rules is a major problem with many data mining techniques. This paper argues that one of the key reasons for the large number of rules is that an inefficient knowledge representation scheme has been used. The current predominant representation of the discovered knowledge is the if ..."
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Cited by 10 (0 self)
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general rules & exceptions. In this representation, a unit of knowledge consists of a single general rule and a set of exceptions. This scheme reduces the complexity of the discovered knowledge substantially. It is also intuitive and easy to understand. This paper focuses on using the representation
KSVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
, 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signalatoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
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Cited by 930 (41 self)
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In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signalatoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many
Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment
 Psychological Review
, 1983
"... Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P(A&B), cannot exceed the probabilities of its constituents, P(A) and.P(B), because the extension (or the possibility set) of the conjunction is included in the exten ..."
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Cited by 427 (4 self)
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in the extension of its constituents. Judgments under uncertainty, however, are often mediated by intuitive heuristics that are not bound by the conjunction rule. A conjunction can be more representative than one of its constituents, and instances of a specific category can be easier to imagine or to retrieve than
A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge
 PSYCHOLOGICAL REVIEW
, 1997
"... How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LS ..."
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Cited by 1772 (10 self)
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How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis
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