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: µ-Charts and Z: hows, whys and wherefores

authors: Reeve, Greg

subjects: computer science

abstract: In this paper we show, by a series of examples, how the µ-chart formalism can be translated into Z. We give reasons for why this is an interesting and sensible thing to do and what it might be used for.

text: µ-Charts and Z: hows, whys and wherefores In this paper we show, by a series of examples, how the µ-chart formalism can be translated into Z. We give reasons for why this is an interesting and sensible thing to do and what it might be used for.

year: 2000-03

title: The science-society relationship in Aotearoa: practicing responsible innovation in the New Zealand research and innovation system

authors: Finlay-Smits, Susanna

subjects: Responsible innovation

abstract: Reports on Aotearoa New Zealand’s research, science, and innovation (RSI) system suggest the sector could improve its social responsibility and create more social value. However, researchers and innovators within the sector find this challenging. Through qualitative interviews and a national survey of RSI system participants, this study explores the current perceptions, practices enacted, and support given around responsible research and innovation practices. Findings indicate that researchers and innovators have diverse understandings of what responsible research and innovation entails, viewing anticipation and reflexivity-aligned practices as the most important aspects of responsible research and innovation. Participants feel most supported by research organisations and funding bodies to enact such aspects of responsible practice. However, they perceive a discrepancy, across all dimensions explored, between what researchers and innovators should do, and what they actually do in terms of enacting responsible practices. Thus, significant room for improvement is identified, particularly regarding practices that align with researchers’ and innovators’ obligations under Te Tiriti o Waitangi, and requirements under Vision Mātauranga.

text: The science-society relationship in Aotearoa: practicing responsible innovation in the New Zealand research and innovation system Reports on Aotearoa New Zealand’s research, science, and innovation (RSI) system suggest the sector could improve its social responsibility and create more social value. However, researchers and innovators within the sector find this challenging. Through qualitative interviews and a national survey of RSI system participants, this study explores the current perceptions, practices enacted, and support given around responsible research and innovation practices. Findings indicate that researchers and innovators have diverse understandings of what responsible research and innovation entails, viewing anticipation and reflexivity-aligned practices as the most important aspects of responsible research and innovation. Participants feel most supported by research organisations and funding bodies to enact such aspects of responsible practice. However, they perceive a discrepancy, across all dimensions explored, between what researchers and innovators should do, and what they actually do in terms of enacting responsible practices. Thus, significant room for improvement is identified, particularly regarding practices that align with researchers’ and innovators’ obligations under Te Tiriti o Waitangi, and requirements under Vision Mātauranga.

year: 2023-05-17