title: Parameter Tuning Using Gaussian Processes

authors: Ma, Jinjin

subjects: Parameter Tunning

abstract: Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter settings generally result in poor modelling. Hence, it is necessary to acquire the “best” parameter values for a particular algorithm before building the model. The “best” model not only reflects the “real” function and is well fitted to existing points, but also gives good performance when making predictions for new points with previously unseen values.

A number of methods exist that have been proposed to optimize parameter values. The basic idea of all such methods is a trial-and-error process whereas the work presented in this thesis employs Gaussian process (GP) regression to optimize the parameter values of a given machine learning algorithm. In this thesis, we consider the optimization of only two-parameter learning algorithms. All the possible parameter values are specified in a 2-dimensional grid in this work. To avoid brute-force search, Gaussian Process Optimization (GPO) makes use of “expected improvement” to pick useful points rather than validating every point of the grid step by step. The point with the highest expected improvement is evaluated using cross-validation and the resulting data point is added to the training set for the Gaussian process model. This process is repeated until a stopping criterion is met. The final model is built using the learning algorithm based on the best parameter values identified in this process.

In order to test the effectiveness of this optimization method on regression and classification problems, we use it to optimize parameters of some well-known machine learning algorithms, such as decision tree learning, support vector machines and boosting with trees. Through the analysis of experimental results obtained on datasets from the UCI repository, we find that the GPO algorithm yields competitive performance compared with a brute-force approach, while exhibiting a distinct advantage in terms of training time and number of cross-validation runs. Overall, the GPO method is a promising method for the optimization of parameter values in machine learning.

text: Parameter Tuning Using Gaussian Processes Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter settings generally result in poor modelling. Hence, it is necessary to acquire the “best” parameter values for a particular algorithm before building the model. The “best” model not only reflects the “real” function and is well fitted to existing points, but also gives good performance when making predictions for new points with previously unseen values.

A number of methods exist that have been proposed to optimize parameter values. The basic idea of all such methods is a trial-and-error process whereas the work presented in this thesis employs Gaussian process (GP) regression to optimize the parameter values of a given machine learning algorithm. In this thesis, we consider the optimization of only two-parameter learning algorithms. All the possible parameter values are specified in a 2-dimensional grid in this work. To avoid brute-force search, Gaussian Process Optimization (GPO) makes use of “expected improvement” to pick useful points rather than validating every point of the grid step by step. The point with the highest expected improvement is evaluated using cross-validation and the resulting data point is added to the training set for the Gaussian process model. This process is repeated until a stopping criterion is met. The final model is built using the learning algorithm based on the best parameter values identified in this process.

In order to test the effectiveness of this optimization method on regression and classification problems, we use it to optimize parameters of some well-known machine learning algorithms, such as decision tree learning, support vector machines and boosting with trees. Through the analysis of experimental results obtained on datasets from the UCI repository, we find that the GPO algorithm yields competitive performance compared with a brute-force approach, while exhibiting a distinct advantage in terms of training time and number of cross-validation runs. Overall, the GPO method is a promising method for the optimization of parameter values in machine learning.

year: 2012

title: Arginase from kiwifruit: properties and seasonal variation

authors: Hale, C.A.

subjects: actinidia deliciosa

abstract: The in vitro activity of arginase (EC 3.5.3.1) was investigated in youngest-mature leaves and roots (1-3 mm diameter) of kiwifruit vines (Actinidia deliciosa var. deliciosa) during an annual growth cycle, and enzyme from root material partially purified. No seasonal trend in the specific activity of arginase was observed in roots. Measurements in leaves, however, rose gradually during early growth and plateaued c. 17 weeks after budbreak. Changes in arginase activity were not correlated with changes in the concentration of arginine (substrate) or glutamine (likely end-product of arginine catabolism) in either tissue during the growth cycle. Purification was by (NH4)2SO4 precipitation and DEAE-cellulose chromatography. The kinetic properties of the enzyme, purified 60-fold over that in crude extracts, indicated a pH optimum of 8.8, and a Km (L-arginine) of 7.85 mM. Partially-purified enzyme was deactivated by dialysis against EDTA, and reactivated in the presence of Mn²⁺, Co²⁺, and Ni²⁺.

text: Arginase from kiwifruit: properties and seasonal variation The in vitro activity of arginase (EC 3.5.3.1) was investigated in youngest-mature leaves and roots (1-3 mm diameter) of kiwifruit vines (Actinidia deliciosa var. deliciosa) during an annual growth cycle, and enzyme from root material partially purified. No seasonal trend in the specific activity of arginase was observed in roots. Measurements in leaves, however, rose gradually during early growth and plateaued c. 17 weeks after budbreak. Changes in arginase activity were not correlated with changes in the concentration of arginine (substrate) or glutamine (likely end-product of arginine catabolism) in either tissue during the growth cycle. Purification was by (NH4)2SO4 precipitation and DEAE-cellulose chromatography. The kinetic properties of the enzyme, purified 60-fold over that in crude extracts, indicated a pH optimum of 8.8, and a Km (L-arginine) of 7.85 mM. Partially-purified enzyme was deactivated by dialysis against EDTA, and reactivated in the presence of Mn²⁺, Co²⁺, and Ni²⁺.

year: 1997

title: Whānau Māori explain how the Harti Hauora Tamariki tool assists better access to health services

authors: Masters-Awatere, Bridgette

abstract: In this paper whānau Māori highlight how a Kaupapa Māori centred intervention (the Harti Hauora Tamariki tool) has improved interactions with health services. The Harti tool is undergoing a Randomised Control Trial at Waikato Hospital in New Zealand. As part of the RCT, the authors engaged in a series of qualitative interviews with whānau members of tamariki Māori (children aged 0-5years) admitted to Waikato Hospital’s paediatric ward. Whānau who met at least one criteria for New Zealand’s domains of deprivation were included. Using a Kaupapa Māori approach to the study, participants shared their views on barriers and facilitators to accessing health resources and primary care services. Our interviews highlight how the Harti tool, when administered in a culturally appropriate and respectful manner that prioritised relationship-building, enabled better connection to healthcare services. Prevalent in our analysis were connections to wider determinants of health and ways to reduce existing health inequities. To conclude the paper, we highlight how the tool has enhanced feelings of being in control of health with the potential to reduce the likelihood of a hospital readmission.

text: Whānau Māori explain how the Harti Hauora Tamariki tool assists better access to health services In this paper whānau Māori highlight how a Kaupapa Māori centred intervention (the Harti Hauora Tamariki tool) has improved interactions with health services. The Harti tool is undergoing a Randomised Control Trial at Waikato Hospital in New Zealand. As part of the RCT, the authors engaged in a series of qualitative interviews with whānau members of tamariki Māori (children aged 0-5years) admitted to Waikato Hospital’s paediatric ward. Whānau who met at least one criteria for New Zealand’s domains of deprivation were included. Using a Kaupapa Māori approach to the study, participants shared their views on barriers and facilitators to accessing health resources and primary care services. Our interviews highlight how the Harti tool, when administered in a culturally appropriate and respectful manner that prioritised relationship-building, enabled better connection to healthcare services. Prevalent in our analysis were connections to wider determinants of health and ways to reduce existing health inequities. To conclude the paper, we highlight how the tool has enhanced feelings of being in control of health with the potential to reduce the likelihood of a hospital readmission.

year: 2019