title: Fully supervised training of Gaussian radial basis function networks in WEKA

authors: Frank, Eibe

subjects: Machine learning

abstract: Radial basis function networks are a type of feedforward network with a long history in machine learning. In spite of this, there is relatively little literature on how to train them so that accurate predictions are obtained. A common strategy is to train the hidden layer of the network using k-means clustering and the output layer using supervised learning. However, Wettschereck and Dietterich found that supervised training of hidden layer parameters can improve predictive performance. They investigated learning center locations, local variances of the basis functions, and attribute weights, in a supervised manner.

This document discusses supervised training of Gaussian radial basis function networks in the WEKA machine learning software. More specifically, we discuss the RBFClassifier and RBFRegressor classes available as part of the RBFNetwork package for WEKA 3.7 and consider (a) learning of center locations and one global variance parameter, (b) learning of center locations and one local variance parameter per basis function, and (c) learning center locations with per-attribute local variance parameters. We also consider learning attribute weights jointly with other parameters.

text: Fully supervised training of Gaussian radial basis function networks in WEKA Radial basis function networks are a type of feedforward network with a long history in machine learning. In spite of this, there is relatively little literature on how to train them so that accurate predictions are obtained. A common strategy is to train the hidden layer of the network using k-means clustering and the output layer using supervised learning. However, Wettschereck and Dietterich found that supervised training of hidden layer parameters can improve predictive performance. They investigated learning center locations, local variances of the basis functions, and attribute weights, in a supervised manner.

This document discusses supervised training of Gaussian radial basis function networks in the WEKA machine learning software. More specifically, we discuss the RBFClassifier and RBFRegressor classes available as part of the RBFNetwork package for WEKA 3.7 and consider (a) learning of center locations and one global variance parameter, (b) learning of center locations and one local variance parameter per basis function, and (c) learning center locations with per-attribute local variance parameters. We also consider learning attribute weights jointly with other parameters.

year: 2014

title: High precision traffic measurement by the WAND research group

authors: Cleary, John G.

subjects: computer science

abstract: Over recent years the size and capacity of the Internet has continued its exponential growth driven by new applications and improving network technology. These changes are particularly significant in the New Zealand context where the high costs of trans-Pacific traffic has mandated that traffic be charged for by volume. This has also lead to a significant focus within the New Zealand Internet community on issues of caching and of careful planning for capacity. 
Approximately three years ago the WAND research group began with a program to measure ATM traffic. We were sharply constrained by cost and decided to start by reprogramming some ATM NIC cards. This paper is largely based on our experience as we have broadened this work to include IP-based non-ATM networks and the construction of our own hardware. We have learned a number of lessons in this work, rediscovering along the way some of the hard discipline that all observation scientists must submit to.

text: High precision traffic measurement by the WAND research group Over recent years the size and capacity of the Internet has continued its exponential growth driven by new applications and improving network technology. These changes are particularly significant in the New Zealand context where the high costs of trans-Pacific traffic has mandated that traffic be charged for by volume. This has also lead to a significant focus within the New Zealand Internet community on issues of caching and of careful planning for capacity. 
Approximately three years ago the WAND research group began with a program to measure ATM traffic. We were sharply constrained by cost and decided to start by reprogramming some ATM NIC cards. This paper is largely based on our experience as we have broadened this work to include IP-based non-ATM networks and the construction of our own hardware. We have learned a number of lessons in this work, rediscovering along the way some of the hard discipline that all observation scientists must submit to.

year: 1999-12

title: Ko au tēnei

authors: Temara, Pou

subjects: Tuhinga auaha

abstract: He puha tēnei, he momo rotarota. E kōrero ana tēnei puha mō te pepeha o Tūhoe, arā, ko Maungapōhatu te maunga, ko Waikare te awa ko Tamakaimoana te hapu, ko Taiturakina rāua ko Pōhokorua ngā tāngata, ko Tūhoe te iwi.

Kua roa tēnei pepeha e mau ana i ngā kākahu Māori o nehe, ā, nō ngā rautau ki muri te tawhito o te pepeha nei. Kua mate noa atu te hunga nā rātau i waihanga. Ko rātau e mōhio ana ki te horopaki i titoa ai ā rātau pepeha. Taka iho ki nāianei kāore anō kia rerekē ngā kupu o te pepeha.

Engari kua rerekē te ao me tōna tai, he whakatipuranga hou kua hua ake hai noho i te whenua. He rerekē ōnā whakaaro, he rerekē te ahurea. Kua roa e noho tahi ana ēnei whakatipuranga me ngā iwi huhua o te ao, he rauiwi te ahurea. Kua whakatōria hoki te whakaaro o aua ahurea maha ki te hinengaro o te Māori. Kua riro ngā whakaaro i te ao whānui koia nei kai te tohutohu i te whakaaro o te Māori o ēnei rā.

Me pēhea te whakauru atu i te whakaaro o mohoā nei ki te whakaaro o ukiuki, kia whai tikanga ai ki te ngākau o nāianei ki te pepeha o mua? Kua tawhito o rātau kākahu. E hia ngā kaupapa, ngā pakanga, ngā kaikiritanga, ngā mamae i roto i taua wā. Kua tae ki te wā hai whakamautanga mā ō nāianei Māori i ngā kākahu o ēnei rā ki ngā pepeha o mua, kia whai wāhi ai ki te ahurea me te whakaaro o ēnei rā.

Ehara i te mea me whakaae te iwi katoa ki ngā whakaaro kua tāpiritia ki te pepeha nei. Engari hai aha mā wai? Ko te rotarota he kupu nā te kaitito, he kupu motuhenga nō tōna ngākau, hai tohu i tāna titiro ki te ao, ā, kia uru atu ai ia ki te ao me ngā whakaaro o mua.

text: Ko au tēnei He puha tēnei, he momo rotarota. E kōrero ana tēnei puha mō te pepeha o Tūhoe, arā, ko Maungapōhatu te maunga, ko Waikare te awa ko Tamakaimoana te hapu, ko Taiturakina rāua ko Pōhokorua ngā tāngata, ko Tūhoe te iwi.

Kua roa tēnei pepeha e mau ana i ngā kākahu Māori o nehe, ā, nō ngā rautau ki muri te tawhito o te pepeha nei. Kua mate noa atu te hunga nā rātau i waihanga. Ko rātau e mōhio ana ki te horopaki i titoa ai ā rātau pepeha. Taka iho ki nāianei kāore anō kia rerekē ngā kupu o te pepeha.

Engari kua rerekē te ao me tōna tai, he whakatipuranga hou kua hua ake hai noho i te whenua. He rerekē ōnā whakaaro, he rerekē te ahurea. Kua roa e noho tahi ana ēnei whakatipuranga me ngā iwi huhua o te ao, he rauiwi te ahurea. Kua whakatōria hoki te whakaaro o aua ahurea maha ki te hinengaro o te Māori. Kua riro ngā whakaaro i te ao whānui koia nei kai te tohutohu i te whakaaro o te Māori o ēnei rā.

Me pēhea te whakauru atu i te whakaaro o mohoā nei ki te whakaaro o ukiuki, kia whai tikanga ai ki te ngākau o nāianei ki te pepeha o mua? Kua tawhito o rātau kākahu. E hia ngā kaupapa, ngā pakanga, ngā kaikiritanga, ngā mamae i roto i taua wā. Kua tae ki te wā hai whakamautanga mā ō nāianei Māori i ngā kākahu o ēnei rā ki ngā pepeha o mua, kia whai wāhi ai ki te ahurea me te whakaaro o ēnei rā.

Ehara i te mea me whakaae te iwi katoa ki ngā whakaaro kua tāpiritia ki te pepeha nei. Engari hai aha mā wai? Ko te rotarota he kupu nā te kaitito, he kupu motuhenga nō tōna ngākau, hai tohu i tāna titiro ki te ao, ā, kia uru atu ai ia ki te ao me ngā whakaaro o mua.

year: 2017