title: Assessment of Self-Management Skills in a Project-Based Learning Paper

authors: Scott, Jonathan B.

abstract: This initial study revealed the potential of having management-specific assessment and business-related demonstrating staff in undergraduate engineering project- based classes. This will offer students valuable insights in preparing for engineering industries that are increasingly incorporating interdisciplinary expertise and ideas to solve complex issues.

text: Assessment of Self-Management Skills in a Project-Based Learning Paper This initial study revealed the potential of having management-specific assessment and business-related demonstrating staff in undergraduate engineering project- based classes. This will offer students valuable insights in preparing for engineering industries that are increasingly incorporating interdisciplinary expertise and ideas to solve complex issues.

year: 2017

title: Instance-based learning: nearest neighbour with generalisation

authors: Martin, Brent

subjects: computer science

abstract: Instance-based learning is a machine learning method that classifies new examples by comparing them to those already seen and in memory. There are two types of instance-based learning; nearest neighbour and case-based reasoning. Of these two methods, nearest neighbour fell into disfavour during the 1980s, but regained popularity recently due to its simplicity and ease of implementation. Nearest neighbour learning is not without problems. It is difficult to define a distance function that works well for both discrete and continuous attributes. Noise and irrelevant attributes also pose problems. Finally, the specificity bias adopted by instance-based learning, while often an advantage, can over-represent small rules at the expense of more general concepts, leading to a marked decrease in classification performance for some domains. Generalised exemplars offer a solution. Examples that share the same class are grouped together, and so represent large rules more fully. This reduces the role of the distance function to determining the class when no rule covers the new example, which reduces the number of classification errors that result from inaccuracies of the distance function, and increases the influence of large rules while still representing small ones. This thesis investigates non-nested generalised exemplars as a way of improving the performance of nearest neighbour. The method is tested using benchmark domains and the results compared with documented results for ungeneralised exemplars, nested generalised exemplars, rule induction methods and a composite rule induction and nearest neighbour learner. The benefits of generalisation are isolated and the performance improvement measured. The results show that non-nested generalisation of exemplars improves the classification performance of nearest neighbour systems and reduces classification time.

text: Instance-based learning: nearest neighbour with generalisation Instance-based learning is a machine learning method that classifies new examples by comparing them to those already seen and in memory. There are two types of instance-based learning; nearest neighbour and case-based reasoning. Of these two methods, nearest neighbour fell into disfavour during the 1980s, but regained popularity recently due to its simplicity and ease of implementation. Nearest neighbour learning is not without problems. It is difficult to define a distance function that works well for both discrete and continuous attributes. Noise and irrelevant attributes also pose problems. Finally, the specificity bias adopted by instance-based learning, while often an advantage, can over-represent small rules at the expense of more general concepts, leading to a marked decrease in classification performance for some domains. Generalised exemplars offer a solution. Examples that share the same class are grouped together, and so represent large rules more fully. This reduces the role of the distance function to determining the class when no rule covers the new example, which reduces the number of classification errors that result from inaccuracies of the distance function, and increases the influence of large rules while still representing small ones. This thesis investigates non-nested generalised exemplars as a way of improving the performance of nearest neighbour. The method is tested using benchmark domains and the results compared with documented results for ungeneralised exemplars, nested generalised exemplars, rule induction methods and a composite rule induction and nearest neighbour learner. The benefits of generalisation are isolated and the performance improvement measured. The results show that non-nested generalisation of exemplars improves the classification performance of nearest neighbour systems and reduces classification time.

year: 1995-05

title: Transformation geometry: Mā te nekehanga, mā te whakaata, mā te hurihanga

authors: Manuel, Hirāni

abstract: The whakataukī (proverb) promotes the notion that navigating obstacles or barriers successfully is a natural part of learning. The learning and teaching of the Māori language and culture are key practices in Māori-medium settings. Teachers in such classrooms are actively promoting academic success as Māori through the medium of te reo Māori (the Māori language). This stance includes the learning of mathematics.

text: Transformation geometry: Mā te nekehanga, mā te whakaata, mā te hurihanga The whakataukī (proverb) promotes the notion that navigating obstacles or barriers successfully is a natural part of learning. The learning and teaching of the Māori language and culture are key practices in Māori-medium settings. Teachers in such classrooms are actively promoting academic success as Māori through the medium of te reo Māori (the Māori language). This stance includes the learning of mathematics.

year: 2015