title: Consumer packaged goods in France: National brands, regional chains and local branding

authors: Ataman, M. Berk

subjects: geography

abstract: The authors document several striking general geographic patterns in the performance of national brands using a large longitudinal scanner database that spans many consumer packaged goods categories and U.S. regional markets. Across markets, they observe that for a typical national brand, the geographic variation in market shares, perceived quality levels, and local dominance is so large that it questions the concept and relevance of a “national brand.” Across time, the authors find that the geographic differences in market shares for national brands are persistent and thus are attributed to “long-term” outcomes. The objective of this article is to open a discussion on these surprising stylized findings related to geography in the food and beverage industries. The authors argue that geographically indexed consumer packaged goods data contain rich information about long-term marketing outcomes that offer several new directions for further research.

text: Consumer packaged goods in France: National brands, regional chains and local branding The authors document several striking general geographic patterns in the performance of national brands using a large longitudinal scanner database that spans many consumer packaged goods categories and U.S. regional markets. Across markets, they observe that for a typical national brand, the geographic variation in market shares, perceived quality levels, and local dominance is so large that it questions the concept and relevance of a “national brand.” Across time, the authors find that the geographic differences in market shares for national brands are persistent and thus are attributed to “long-term” outcomes. The objective of this article is to open a discussion on these surprising stylized findings related to geography in the food and beverage industries. The authors argue that geographically indexed consumer packaged goods data contain rich information about long-term marketing outcomes that offer several new directions for further research.

year: 2007

title: MaxGain: Regularisation of neural networks by constraining activation magnitudes

authors: Gouk, Henry

subjects: Machine learning

abstract: Effective regularisation of neural networks is essential to combat overfitting due to the large number of parameters involved. We present an empirical analogue to the Lipschitz constant of a feed-forward neural network, which we refer to as the maximum gain. We hypothesise that constraining the gain of a network will have a regularising effect, similar to how constraining the Lipschitz constant of a network has been shown to improve generalisation. A simple algorithm is provided that involves rescaling the weight matrix of each layer after each parameter update. We conduct a series of studies on common benchmark datasets, and also a novel dataset that we introduce to enable easier significance testing for experiments using convolutional networks. Performance on these datasets compares favourably with other common regularisation techniques. Data related to this paper is available at: https://www.cs.waikato.ac.nz/~ml/sins10/.

text: MaxGain: Regularisation of neural networks by constraining activation magnitudes Effective regularisation of neural networks is essential to combat overfitting due to the large number of parameters involved. We present an empirical analogue to the Lipschitz constant of a feed-forward neural network, which we refer to as the maximum gain. We hypothesise that constraining the gain of a network will have a regularising effect, similar to how constraining the Lipschitz constant of a network has been shown to improve generalisation. A simple algorithm is provided that involves rescaling the weight matrix of each layer after each parameter update. We conduct a series of studies on common benchmark datasets, and also a novel dataset that we introduce to enable easier significance testing for experiments using convolutional networks. Performance on these datasets compares favourably with other common regularisation techniques. Data related to this paper is available at: https://www.cs.waikato.ac.nz/~ml/sins10/.

year: 2019

title: Te Tikanga me nga Kawa

authors: Temara, Pou

subjects: Tikanga

abstract: Ko te tikanga te kaitohu i te huarahi tika e pai ai te whakahaere a te Māori i a ia anō. Ki te kore he tikanga e kore e pai tāna noho, ka mate tōna mauri, te mutunga atu ko tōna toiora ka mate. Ki te kore he tikanga, ka  tūtakarerewa te kāhui. He pērā anō te kawa. Ki te kore he kawa, ko taua āhua anō o te pāihiihi o te whakaaro, ko te noho i roto o te kōpā, te mutunga atu ko te pakanga. Engari he nui ngā pōhēhētanga mō te tikanga me te kawa. Ko ētahi tohunga e kī ana he tapu kē atu te kawa i te tikanga, ko ētahi anō e tohe ana mō te tapu o te tikanga. He tohunga anō e kōkiri ana i te haumanutanga o te tikanga me te tāharaharatanga o te kawa. Kai tēnei tuhinga e takoto ai ngā whakamārama mō te tikanga me te kawa me te rerekētanga o tētahi i tētahi.

text: Te Tikanga me nga Kawa Ko te tikanga te kaitohu i te huarahi tika e pai ai te whakahaere a te Māori i a ia anō. Ki te kore he tikanga e kore e pai tāna noho, ka mate tōna mauri, te mutunga atu ko tōna toiora ka mate. Ki te kore he tikanga, ka  tūtakarerewa te kāhui. He pērā anō te kawa. Ki te kore he kawa, ko taua āhua anō o te pāihiihi o te whakaaro, ko te noho i roto o te kōpā, te mutunga atu ko te pakanga. Engari he nui ngā pōhēhētanga mō te tikanga me te kawa. Ko ētahi tohunga e kī ana he tapu kē atu te kawa i te tikanga, ko ētahi anō e tohe ana mō te tapu o te tikanga. He tohunga anō e kōkiri ana i te haumanutanga o te tikanga me te tāharaharatanga o te kawa. Kai tēnei tuhinga e takoto ai ngā whakamārama mō te tikanga me te kawa me te rerekētanga o tētahi i tētahi.

year: 2011