title: Good lattice rules based on the general weighted star discrepancy

authors: Sinescu, Vasile

subjects: mathematics

abstract: We study the problem of constructing rank-  lattice rules which have good bounds on the ``weighted star discrepancy''. Here the non-negative weights are general weights rather than the product weights considered in most earlier works. In order to show the existence of such good lattice rules, we use an averaging argument, and a similar argument is used later to prove that these lattice rules may be obtained using a component-by-component (CBC) construction of the generating vector. Under appropriate conditions on the weights, these lattice rules satisfy strong tractability bounds on the weighted star discrepancy. Particular classes of weights known as ``order-dependent'' and ``finite-order'' weights are then considered and we show that the cost of the construction can be very much reduced for these two classes of weights.

text: Good lattice rules based on the general weighted star discrepancy We study the problem of constructing rank-  lattice rules which have good bounds on the ``weighted star discrepancy''. Here the non-negative weights are general weights rather than the product weights considered in most earlier works. In order to show the existence of such good lattice rules, we use an averaging argument, and a similar argument is used later to prove that these lattice rules may be obtained using a component-by-component (CBC) construction of the generating vector. Under appropriate conditions on the weights, these lattice rules satisfy strong tractability bounds on the weighted star discrepancy. Particular classes of weights known as ``order-dependent'' and ``finite-order'' weights are then considered and we show that the cost of the construction can be very much reduced for these two classes of weights.

year: 2007-04

title: Feature selection for discrete and numeric class machine learning

authors: Hall, Mark A.

subjects: computer science

abstract: Algorithms for feature selection fall into two broad categories: wrappers use the learning algorithm itself to evaluate the usefulness of features, while filters evaluate features according to heuristics based on general characteristics of the data. For application to large databases, filters have proven to be more practical than wrappers because they are much faster. However, most existing filter algorithms only work with discrete classification problems. 
This paper describes a fast, correlation-based filter algorithm that can be applied to continuous and discrete problems. Experiments using the new method as a preprocessing step for naïve Bayes, instance-based learning, decision trees, locally weighted regression, and model trees show it to be an effective feature selector - it reduces the data in dimensionality by more than sixty percent in most cases without negatively affecting accuracy. Also, decision and model trees built from the pre-processed data are often significantly smaller.

text: Feature selection for discrete and numeric class machine learning Algorithms for feature selection fall into two broad categories: wrappers use the learning algorithm itself to evaluate the usefulness of features, while filters evaluate features according to heuristics based on general characteristics of the data. For application to large databases, filters have proven to be more practical than wrappers because they are much faster. However, most existing filter algorithms only work with discrete classification problems. 
This paper describes a fast, correlation-based filter algorithm that can be applied to continuous and discrete problems. Experiments using the new method as a preprocessing step for naïve Bayes, instance-based learning, decision trees, locally weighted regression, and model trees show it to be an effective feature selector - it reduces the data in dimensionality by more than sixty percent in most cases without negatively affecting accuracy. Also, decision and model trees built from the pre-processed data are often significantly smaller.

year: 1999-04

title: Tū Māori Ake: Seeking which is above.

authors: Maxwell, Te Kahautu

abstract: Te Waka Āwhina 2015 Seeking that which is above Kaupapa - Philosophy o Aotearoa “Tū Māori ake” E ngā mana, e ngā reo, e ngā karangatanga, nei rā te powhiri ki a koutou katoa ‘MĀ TE MĀORI, MŌ TE MĀORI’ Te Waka Āwhina o Aotearoa National Training Hui by Māori for Māori within local government Āwhea: 2 – 4 February 2015 Ki whea: The Gallagher Academy of Performing Arts, The University of Waikato, Hamilton Hau kāinga: Te Puku o te Ika.

text: Tū Māori Ake: Seeking which is above. Te Waka Āwhina 2015 Seeking that which is above Kaupapa - Philosophy o Aotearoa “Tū Māori ake” E ngā mana, e ngā reo, e ngā karangatanga, nei rā te powhiri ki a koutou katoa ‘MĀ TE MĀORI, MŌ TE MĀORI’ Te Waka Āwhina o Aotearoa National Training Hui by Māori for Māori within local government Āwhea: 2 – 4 February 2015 Ki whea: The Gallagher Academy of Performing Arts, The University of Waikato, Hamilton Hau kāinga: Te Puku o te Ika.

year: 2015-02-03