title: Correlation-based feature selection of discrete and numeric class machine learning

authors: Hall, Mark A.

subjects: computer science

abstract: Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that 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. The algorithm often out-performs the well-known ReliefF attribute estimator when used as a preprocessing step for naive Bayes, instance-based learning, decision trees, locally weighted regression, and model trees. It performs more feature selection than ReliefF does-reducing the data dimensionality by fifty percent in most cases. Also, decision and model trees built from the preprocessed data are often significantly smaller.

text: Correlation-based feature selection of discrete and numeric class machine learning Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that 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. The algorithm often out-performs the well-known ReliefF attribute estimator when used as a preprocessing step for naive Bayes, instance-based learning, decision trees, locally weighted regression, and model trees. It performs more feature selection than ReliefF does-reducing the data dimensionality by fifty percent in most cases. Also, decision and model trees built from the preprocessed data are often significantly smaller.

year: 2000-05

title: Generalised Nonblocking

authors: Malik, Robi

subjects: computer science

abstract: This paper studies the nonblocking check used in supervisory control of discrete event systems and its limitations. Different examples with different liveness requirements are discussed. It is shown that the standard nonblocking check can be used to specify most requirements of interest, but that it lacks expressive power in a few cases. A generalised nonblocking check is proposed to overcome the weakness, and its relationship to standard nonblocking is explored. Results suggest that generalised nonblocking, while having the same useful properties with respect to synthesis and compositional verification, can provide for more concise problem representations in some cases.

text: Generalised Nonblocking This paper studies the nonblocking check used in supervisory control of discrete event systems and its limitations. Different examples with different liveness requirements are discussed. It is shown that the standard nonblocking check can be used to specify most requirements of interest, but that it lacks expressive power in a few cases. A generalised nonblocking check is proposed to overcome the weakness, and its relationship to standard nonblocking is explored. Results suggest that generalised nonblocking, while having the same useful properties with respect to synthesis and compositional verification, can provide for more concise problem representations in some cases.

year: 2008

title: Te Whare o Te Kahikatoa: He Kura nui, he Kura roa, he Kura Parawhakawai

authors: Rangihau, Ngatai

subjects: Kahikatoa

abstract: “Te Whare o te Kahikatoa,
Te Iho o te Maire,
Te Pōkaitāniwhaniwha o te Kohu”
(TKPOT, 2010-2016).¹

Ka hoki nei kia Te Maunga rāua ko Hinepukohurangi, ki te taetanga mai hoki o Mataatua waka², ko ngā kura o tawhito, kura wānanga, kura parawhakawai o te iwi o Tūhoe ngā tino whare whakapakari pōkaitāniwhaniwha mō roto i te whare o te kahikatoa. Īnā te rahi o ngā toataumatarau i puta ngā ihu, ka tū rātau hai toa rangatira ārahi i te iwi. Ki te kore ēneki kura o te nehenehe kāre he oranga, kāre e eke te iwi ngā whakairinga tūmanako e wawatatia ana e rātau.
 
Waihoki, ko te kaupapa e rangahau ana i tēnei tuhinga he whakapono, he whakapae, kai konei tonu tēnei hua o te wānanga. He kura hai whakapakari rangatira mō āpōpō. Ahakoa tēnei ao hurihuri koinei tonu tētahi huarahi nā te Māori, nā Tūhoe hai whakaohooho mā ngā uri whakaheke, e mau pū ai a Ngāi Tūhoe ki ōna kura tapu o nehe.

text: Te Whare o Te Kahikatoa: He Kura nui, he Kura roa, he Kura Parawhakawai “Te Whare o te Kahikatoa,
Te Iho o te Maire,
Te Pōkaitāniwhaniwha o te Kohu”
(TKPOT, 2010-2016).¹

Ka hoki nei kia Te Maunga rāua ko Hinepukohurangi, ki te taetanga mai hoki o Mataatua waka², ko ngā kura o tawhito, kura wānanga, kura parawhakawai o te iwi o Tūhoe ngā tino whare whakapakari pōkaitāniwhaniwha mō roto i te whare o te kahikatoa. Īnā te rahi o ngā toataumatarau i puta ngā ihu, ka tū rātau hai toa rangatira ārahi i te iwi. Ki te kore ēneki kura o te nehenehe kāre he oranga, kāre e eke te iwi ngā whakairinga tūmanako e wawatatia ana e rātau.
 
Waihoki, ko te kaupapa e rangahau ana i tēnei tuhinga he whakapono, he whakapae, kai konei tonu tēnei hua o te wānanga. He kura hai whakapakari rangatira mō āpōpō. Ahakoa tēnei ao hurihuri koinei tonu tētahi huarahi nā te Māori, nā Tūhoe hai whakaohooho mā ngā uri whakaheke, e mau pū ai a Ngāi Tūhoe ki ōna kura tapu o nehe.

year: 2017