title: Structure from randomness in halfspace learning with the zero-one loss

authors: Kabán, Ata

abstract: We prove risk bounds for halfspace learning when the data dimensionality is allowed to be larger than the sample size, using a notion of compressibility by random projection. In particular, we give upper bounds for the empirical risk minimizer learned efficiently from randomly projected data, as well as uniform upper bounds in the full high-dimensional space. Our main findings are the following: i) In both settings, the obtained bounds are able to discover and take advantage of benign geometric structure, which turns out to depend on the cosine similarities between the classifier and points of the input space, and provide a new interpretation of margin distribution type arguments. ii) Furthermore our bounds allow us to draw new connections between several existing successful classification algorithms, and we also demonstrate that our theory is predictive of empirically observed performance in numerical simulations and experiments. iii) Taken together, these results suggest that the study of compressive learning can improve our understanding of which benign structural traits – if they are possessed by the data generator – make it easier to learn an effective classifier from a sample.

text: Structure from randomness in halfspace learning with the zero-one loss We prove risk bounds for halfspace learning when the data dimensionality is allowed to be larger than the sample size, using a notion of compressibility by random projection. In particular, we give upper bounds for the empirical risk minimizer learned efficiently from randomly projected data, as well as uniform upper bounds in the full high-dimensional space. Our main findings are the following: i) In both settings, the obtained bounds are able to discover and take advantage of benign geometric structure, which turns out to depend on the cosine similarities between the classifier and points of the input space, and provide a new interpretation of margin distribution type arguments. ii) Furthermore our bounds allow us to draw new connections between several existing successful classification algorithms, and we also demonstrate that our theory is predictive of empirically observed performance in numerical simulations and experiments. iii) Taken together, these results suggest that the study of compressive learning can improve our understanding of which benign structural traits – if they are possessed by the data generator – make it easier to learn an effective classifier from a sample.

year: 2020

title: A process for the development and application of simulation models in applied economics

authors: Doole, Graeme J.

subjects: modelling

abstract: Simulation models are widely used in applied economics to improve understanding of how a system could behave under different conditions. However, the potential degree to which such models can influence decision making depends on their ability to provide an adequate description of the important elements of a given problem. A systematic and robust procedure for the development and application of such models in agricultural, ecological, environmental and natural resource economics is presented. This process is based on the authors' experience across a broad range of model types and applications and extensive literature review. The practical impact of simulation models is argued to be greater where stakeholders and technical experts are consulted extensively throughout the modelling process.

text: A process for the development and application of simulation models in applied economics Simulation models are widely used in applied economics to improve understanding of how a system could behave under different conditions. However, the potential degree to which such models can influence decision making depends on their ability to provide an adequate description of the important elements of a given problem. A systematic and robust procedure for the development and application of such models in agricultural, ecological, environmental and natural resource economics is presented. This process is based on the authors' experience across a broad range of model types and applications and extensive literature review. The practical impact of simulation models is argued to be greater where stakeholders and technical experts are consulted extensively throughout the modelling process.

year: 2013

title: Exploring synergies between Māori pedagogy and communicative language teaching

authors: Nock, Sophie

subjects: Māori pedagogy

abstract: The primary aim of this paper is to explore potential synergies between Māori pedagogy and what is known as ‘communicative language teaching’. A brief outline of some changes and developments in approaches to language teaching and learning over the last few decades is followed by a summary of some of the main characteristics of communicative language teaching (CLT) and of different approaches to the conceptualization of Māori pedagogy. Some of the primary characteristics of communicative language teaching are then explored in the context of Māori pedagogy and it is argued that there are some important synergies between the two.

text: Exploring synergies between Māori pedagogy and communicative language teaching The primary aim of this paper is to explore potential synergies between Māori pedagogy and what is known as ‘communicative language teaching’. A brief outline of some changes and developments in approaches to language teaching and learning over the last few decades is followed by a summary of some of the main characteristics of communicative language teaching (CLT) and of different approaches to the conceptualization of Māori pedagogy. Some of the primary characteristics of communicative language teaching are then explored in the context of Māori pedagogy and it is argued that there are some important synergies between the two.

year: 2009-02