The Experts below are selected from a list of 67944 Experts worldwide ranked by ideXlab platform

Rajarshi Guha - One of the best experts on this subject based on the ideXlab platform.

  • License GPL (> = 2)
    2013
    Co-Authors: Udaya B. Kogalur, Rajarshi Guha
    Abstract:

    Description The Predictive Modelling Markup Language (PMML) is a language for representing models, in an application independent way. Such models can then be loaded into other applications supporting PMML, including ADAPA from Zementis, Teradata Warehouse Miner and IBM’s DB2. The package provides a Generic pmml Function to generate pmml for an object. Using a S3 Generic Function the appropriate method for the class of the supplied object is dispatched. The package currently supports the export of PMML for linear regression, generalized linear regression, multinomial logistic regression, SVMs, rpart classification trees, randomSurvivalForest, randomForest, and kmeans clusters. This package is part of the Rattle toolkit

  • License GPL (> = 2)
    2011
    Co-Authors: Udaya B. Kogalur, Rajarshi Guha
    Abstract:

    Description The Predictive Modelling Markup Language (PMML) is a language for representing models, in an application independent way. Such models can then be loaded into other applications supporting PMML, including ADAPA from Zementis, Teradata Warehouse Miner and IBM's DB2. The package provides a Generic pmml Function to generate pmml for an object. Using a S3 Generic Function the approriate method for the class of the supplied object is dispatched. The package currently supports the export of PMML for linear regression, SVMs, rpart classification trees, randomSurvivalForest forest models, and kmeans clusters. This package is part of the Rattle toolkit

Simon Jones - One of the best experts on this subject based on the ideXlab platform.

  • scrap your boilerplate with class extensible Generic Functions
    International Conference on Functional Programming, 2005
    Co-Authors: Ralf Lammel, Simon Jones
    Abstract:

    The 'Scrap your boilerplate' approach to Generic programming allows the programmer to write Generic Functions that can traverse arbitrary data structures, and yet have type-specific cases. However, the original approach required all the type-specific cases to be supplied at once, when the recursive knot of Generic Function definition is tied. Hence, Generic Functions were closed. In contrast, Haskell's type classes support open, or extensible, Functions that can be extended with new type-specific cases as new data types are defined. In this paper, we extend the 'Scrap your boilerplate' approach to support this open style. On the way, we demonstrate the desirability of abstraction over type classes, and the usefulness of recursive dictionarie.

Ralf Hinze - One of the best experts on this subject based on the ideXlab platform.

  • a new approach to Generic Functional programming
    Symposium on Principles of Programming Languages, 2000
    Co-Authors: Ralf Hinze
    Abstract:

    This paper describes a new approach to Generic Functional programming, which allows us to define Functions Generically for all datatypes expressible in Haskell. A Generic Function is one that is defined by induction on the structure of types. Typical examples include pretty printers, parsers, and comparison Functions. The advanced type system of Haskell presents a real challenge: datatypes may be parameterized not only by types but also by type constructors, type definitions may involve mutual recursion, and recursive calls of type constructors can be arbitrarily nested. We show that—despite this complexity—a Generic Function is uniquely defined by giving cases for primitive types and type constructors (such as disjoint unions and cartesian products). Given this information a Generic Function can be specialized to arbitrary Haskell datatypes. The key idea of the approach is to model types by terms of the simply typed λ-calculus augmented by a family of recursion operators. While conceptually simple, our approach places high demands on the type system: it requires polymorphic recursion, rank- n types, and a strong form of type constructor polymorphism. Finally, we point out connections to Haskell's class system and show that our approach generalizes type classes in some respects.

Rinkus, Gerard J. - One of the best experts on this subject based on the ideXlab platform.

  • A cortical sparse distributed coding model linking mini- and macrocolumn-scale Functionality
    'Frontiers Media SA', 2017
    Co-Authors: Rinkus, Gerard J.
    Abstract:

    No Generic Function for the minicolumn, i.e., one that would apply equally well to all cortical areas and species, has yet been proposed. I propose that the minicolumn does have a Generic Functionality, which only becomes clear when seen in the context of the Function of the higher-level, subsuming unit, the macrocolumn. I propose that: a) a macrocolumn's Function is to store sparse distributed representations of its inputs and to be a recognizer of those inputs; and b) the Generic Function of the minicolumn is to enforce macrocolumnar code sparseness. The minicolumn, defined here as a physically localized pool of ~20 L2/3 pyramidals, does this by acting as a winner-take-all (WTA) competitive module, implying that macrocolumnar codes consist of ~70 active L2/3 cells, assuming ~70 minicolumns per macrocolumn. I describe an algorithm for activating these codes during both learning and retrievals, which causes more similar inputs to map to more highly intersecting codes, a property which yields ultra-fast (immediate, first-shot) storage and retrieval. The algorithm achieves this by adding an amount of randomness (noise) into the code selection process, which is inversely proportional to an input's familiarity. I propose a possible mapping of the algorithm onto cortical circuitry, and adduce evidence for a neuromodulatory implementation of this familiarity-contingent noise mechanism. The model is distinguished from other recent columnar cortical circuit models in proposing a Generic minicolumnar Function in which a group of cells within the minicolumn, the L2/3 pyramidals, compete (WTA) to be part of the sparse distributed macrocolumnar code.Comment: 13 pages, 5 figure

  • A Cortical Sparse Distributed Coding Model Linking Mini- and Macrocolumn-Scale Functionality
    Frontiers Research Foundation, 2026
    Co-Authors: Rinkus, Gerard J.
    Abstract:

    No Generic Function for the minicolumn – i.e., one that would apply equally well to all cortical areas and species – has yet been proposed. I propose that the minicolumn does have a Generic Functionality, which only becomes clear when seen in the context of the Function of the higher-level, subsuming unit, the macrocolumn. I propose that: (a) a macrocolumn's Function is to store sparse distributed representations of its inputs and to be a recognizer of those inputs; and (b) the Generic Function of the minicolumn is to enforce macrocolumnar code sparseness. The minicolumn, defined here as a physically localized pool of ∼20 L2/3 pyramidals, does this by acting as a winner-take-all (WTA) competitive module, implying that macrocolumnar codes consist of ∼70 active L2/3 cells, assuming ∼70 minicolumns per macrocolumn. I describe an algorithm for activating these codes during both learning and retrievals, which causes more similar inputs to map to more highly intersecting codes, a property which yields ultra-fast (immediate, first-shot) storage and retrieval. The algorithm achieves this by adding an amount of randomness (noise) into the code selection process, which is inversely proportional to an input's familiarity. I propose a possible mapping of the algorithm onto cortical circuitry, and adduce evidence for a neuromodulatory implementation of this familiarity-contingent noise mechanism. The model is distinguished from other recent columnar cortical circuit models in proposing a Generic minicolumnar Function in which a group of cells within the minicolumn, the L2/3 pyramidals, compete (WTA) to be part of the sparse distributed macrocolumnar code

Udaya B. Kogalur - One of the best experts on this subject based on the ideXlab platform.

  • License GPL (> = 2)
    2013
    Co-Authors: Udaya B. Kogalur, Rajarshi Guha
    Abstract:

    Description The Predictive Modelling Markup Language (PMML) is a language for representing models, in an application independent way. Such models can then be loaded into other applications supporting PMML, including ADAPA from Zementis, Teradata Warehouse Miner and IBM’s DB2. The package provides a Generic pmml Function to generate pmml for an object. Using a S3 Generic Function the appropriate method for the class of the supplied object is dispatched. The package currently supports the export of PMML for linear regression, generalized linear regression, multinomial logistic regression, SVMs, rpart classification trees, randomSurvivalForest, randomForest, and kmeans clusters. This package is part of the Rattle toolkit

  • License GPL (> = 2)
    2011
    Co-Authors: Udaya B. Kogalur, Rajarshi Guha
    Abstract:

    Description The Predictive Modelling Markup Language (PMML) is a language for representing models, in an application independent way. Such models can then be loaded into other applications supporting PMML, including ADAPA from Zementis, Teradata Warehouse Miner and IBM's DB2. The package provides a Generic pmml Function to generate pmml for an object. Using a S3 Generic Function the approriate method for the class of the supplied object is dispatched. The package currently supports the export of PMML for linear regression, SVMs, rpart classification trees, randomSurvivalForest forest models, and kmeans clusters. This package is part of the Rattle toolkit