Results 1  10
of
16
Quantization
 IEEE TRANS. INFORM. THEORY
, 1998
"... The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modula ..."
Abstract

Cited by 639 (11 self)
 Add to MetaCart
The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modulation systems, especially in the 1948 paper of Oliver, Pierce, and Shannon. Also in 1948, Bennett published the first highresolution analysis of quantization and an exact analysis of quantization noise for Gaussian processes, and Shannon published the beginnings of rate distortion theory, which would provide a theory for quantization as analogtodigital conversion and as data compression. Beginning with these three papers of fifty years ago, we trace the history of quantization from its origins through this decade, and we survey the fundamentals of the theory and many of the popular and promising techniques for quantization.
Iterate: A conceptual clustering algorithm for data mining
 IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS
, 1998
"... The data exploration task can be divided into three interrelated subtasks: (i) feature selection, (ii) discovery, and (iii) interpretation. This paper describes an unsupervised discovery method with biases geared toward partitioning objects into clusters that improve interpretability. The algorithm, ..."
Abstract

Cited by 19 (0 self)
 Add to MetaCart
The data exploration task can be divided into three interrelated subtasks: (i) feature selection, (ii) discovery, and (iii) interpretation. This paper describes an unsupervised discovery method with biases geared toward partitioning objects into clusters that improve interpretability. The algorithm, ITERATE, employs: (i) a data ordering scheme and (ii) an iterative redistribution operator to produce maximally cohesive and distinct clusters. Cohesion or intraclass similarity is measured in terms of the match between individual objects and their assigned cluster prototype. Distinctness or interclass dissimilarity is measured by an average of the variance of the distribution matchbetween clusters. We demonstrate that interpretability, from a problem solving viewpoint, is addressed by theintra and interclass measures. Empirical results demonstrate the properties of the discovery algorithm, and its applications to problem solving.
Iterate: A conceptual clustering method for knowledge discovery in databases
 In Braunschweig, B., & Day, R. (Eds.), Innovative Applications of Artificial Intelligence in the Oil and Gas Industry
, 1995
"... ..."
An Application of Econometric Models to International Marketing
 Journal of Marketing Research
, 1970
"... This article considers the various ways in which firms might estimate market size by country, with particular consideration given to the use of econometric models. The article aims at three related questions. First, what has happened over the past thirty years in the use of econometric models for me ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
This article considers the various ways in which firms might estimate market size by country, with particular consideration given to the use of econometric models. The article aims at three related questions. First, what has happened over the past thirty years in the use of econometric models for measuring geographical markets? Second, is it possible to demonstrate that currently available econometric techniques lead to "improved" measurement of geographical marketsand, in particular, for international markets? Finally, have advances in applied econometric analysis over the past thirty years led to any demonstrable progress in measuring gem graphical markets? Methods For Measuring Sales Rates By Country Trade and Production Data
Exploratory Analysis of Marketing Data: Trees vs. Regression
"... This article compares the predictive ability of models developed by two different statistical methods, tree analysis and regression analysis. Each was used in an exploratory study to develop a model to make predictions for a specific marketing situation. The Statistical Methods The regression model ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
This article compares the predictive ability of models developed by two different statistical methods, tree analysis and regression analysis. Each was used in an exploratory study to develop a model to make predictions for a specific marketing situation. The Statistical Methods The regression model is well known and no description is provided here. Tree analysis, however, is less well known. To add to the confusion, it has been labeled in a number o £ ways – e.g., multiple classification, multilevel crosstabulations, or configurational analysis. Whatever the names, the basic idea is to classify objects in cells so that the objects in the cells are similar to one another yet different from the objects in other cells. Similarity is judged by the score on a given dependent or criterion variable (which differentiates this method from cluster or factor analysis, where the similarity is based only upon scores on a set of descriptive variables). Tree analysis is an extension to n variables of the simple crossclassification approach. Consider the following example: a researcher is studying the factors which determine whether a family owns two or more automobiles. He finds that income may be used to classify respondents. Illustrative results for his sample are provided in Figure 1. He then decides that the number of drivers in the family may also be important for highincome families.
Extending Iterate Conceptual Clustering Scheme In Dealing With Numeric Data
, 1995
"... ion and Interpretation Clustering Meaningful Clusters with Interpretations Figure 1: The Key Steps in Conceptual Clustering Systems grouping the data objects into clusters or groups based on the similarity of properties among the objects. The goal is to derive more general concepts that describe the ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
ion and Interpretation Clustering Meaningful Clusters with Interpretations Figure 1: The Key Steps in Conceptual Clustering Systems grouping the data objects into clusters or groups based on the similarity of properties among the objects. The goal is to derive more general concepts that describe the problem solving task. The task of interpretation involves determining whether the induced concepts are useful for the problem solving tasks that the user is interested in. This task involves the examination of the intentional description of a class in the context of background knowledge about the domain. Overview of the Clustering Methods Traditional approaches to cluster analysis (numerical taxonomy) represent the objects to be clustered as points in a multidimensional metric space and adopt distance metrics, such as Euclidean and Mahalanobis measures, to define dissimilarity between objects. Cluster analysis methods take on one of two different forms: 1. parametric methods: they assume t...
C.2 LANDSAT Imaging Project
"... class for SeedMakerBase. Generell functionality to store a solution and the instance. // Derived classes must provide MakeSeed which is providing a new SeedSolution. // ==================================================================================================== class SeedMakerBase  public: ..."
Abstract
 Add to MetaCart
class for SeedMakerBase. Generell functionality to store a solution and the instance. // Derived classes must provide MakeSeed which is providing a new SeedSolution. // ==================================================================================================== class SeedMakerBase  public: SeedMakerBase (MinWOutlierProblem &Inst) : Inst(Inst)  SolData = NULL; ~SeedMakerBase ()  MinWOutlierProblem &Instance ()  return Inst; virtual void MakeSeed (MixedSolution &SeedSol) = 0; // Start the creation of the seeds void CollectData (MixedSolution &Sol)  if (SolData != NULL) delete SolData; SolData = new MixedSolution (Sol); void CleanSolution (MixedSolution &Solution); // Empty a solution protected: int FindAvailableCluster(MixedSolution &Solution); private: MixedSolution *SolData; MinWOutlierProblem &Inst; // Local storage of the reference for the problem ; // ==================================================================================================== // Fill a given solution with random seeds. // ==================================================================================================== class RandomSeedsMaker : public SeedMakerBase  public: RandomSeedsMaker (MinWOutlierProblem& Inst, int a); ~RandomSeedsMaker ()  void MakeSeed (MixedSolution &SeedSol); // Start the seed making void SetRandomSeed (int a)  RandomSeed = a; // Set the seed for the random number stream void SetSeedsize (int a)  // Set the seed size #ifdef DEBUG assert (a ? Instance().X().p()); #endif SeedSize = a; private: int SeedSize; long RandomSeed; ; // ==================================================================================================== // Generates just one Seed in the solution vector // ===========================================================...
AUTOMATIC CLASSIFICATION
"... In this chapter I shall attempt to present a coherent account of classification in such a way that the principles involved will be sufficiently understood for anyone wishing to use classification techniques in IR to do so without too much difficulty. The emphasis will be ..."
Abstract
 Add to MetaCart
In this chapter I shall attempt to present a coherent account of classification in such a way that the principles involved will be sufficiently understood for anyone wishing to use classification techniques in IR to do so without too much difficulty. The emphasis will be
An Autonomous Reading Machine
"... AbstractAn unconventional approach to character recognition is developed. The resulting system is based solely on the statistical properties of the language, therefore it can read printed text with no previous training or a priori information about the structure of the characters. The known letter ..."
Abstract
 Add to MetaCart
AbstractAn unconventional approach to character recognition is developed. The resulting system is based solely on the statistical properties of the language, therefore it can read printed text with no previous training or a priori information about the structure of the characters. The known letterpair frequencies of the language are used to identify the printed symbols in the following manner. First, the scanned characters are partitioned into distinct groups of similar patterns by means of a distance measure. Each class (at most 26 are permitted) is assigned an arbitrary label, and an intermediate tape, containing these temporary labels of the symbols in the original sequence, is generated. In the second phase of the program, the matrix of bigram frequencies of the labels is compared to a frequency matrix obtained from a large sample of English text. The labels are then assigned alphabetic symbols in such a way that the correspondence between the two matrices is maximized. The method is tested on a 100 000character data set comprising four markedly different fonts. Index TermsAdaptive, character recognition, clustering, cryptograms, linear categorizer, pattern classification, reading machine.