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Articles 1 - 8 of 8
Full-Text Articles in Computer Engineering
Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh
Conference papers
Accurate classification of astronomical objects currently relies on spectroscopic data. Acquiring this data is time-consuming and expensive compared to photometric data. Hence, improving the accuracy of photometric classification could lead to far better coverage and faster classification pipelines. This paper investigates the benefit of using unsupervised feature-extraction from multi-wavelength image data for photometric classification of stars, galaxies and QSOs. An unsupervised Deep Belief Network is used, giving the model a higher level of interpretability thanks to its generative nature and layer-wise training. A Random Forest classifier is used to measure the contribution of the novel features compared to a set …
Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee
Conference papers
Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated the development of the semi-supervised learning problem domain, which makes use of unlabelled data — in conjunction with a small amount of labelled data — to infer the correct labels of a partially labelled dataset. Active Learning is one of the most successful approaches to semi-supervised learning, and has been shown to reduce the cost and time taken to produce a fully labelled dataset. In this paper we present Activist; a free, online, state-of-the-art platform which leverages active learning techniques to improve the efficiency of …
Empirical Comparative Analysis Of 1-Of-K Coding And K-Prototypes In Categorical Clustering, Fei Wang, Hector Franco, John Pugh, Robert J. Ross
Empirical Comparative Analysis Of 1-Of-K Coding And K-Prototypes In Categorical Clustering, Fei Wang, Hector Franco, John Pugh, Robert J. Ross
Conference papers
Clustering is a fundamental machine learning application, which partitions data into homogeneous groups. K-means and its variants are the most widely used class of clustering algorithms today. However, the original k-means algorithm can only be applied to numeric data. For categorical data, the data has to be converted into numeric data through 1-of-K coding which itself causes many problems. K-prototypes, another clustering algorithm that originates from the k-means algorithm, can handle categorical data by adopting a different notion of distance. In this paper, we systematically compare these two methods through an experimental analysis. Our analysis shows that K-prototypes is more …
Model-Free And Model-Based Active Learning For Regression, Jack O'Neill, Sarah Jane Delany, Brian Macnamee
Model-Free And Model-Based Active Learning For Regression, Jack O'Neill, Sarah Jane Delany, Brian Macnamee
Conference papers
Training machine learning models often requires large labelled datasets, which can be both expensive and time-consuming to obtain. Active learning aims to selectively choose which data is labelled in order to minimize the total number of labels required to train an effective model. This paper compares model-free and model-based approaches to active learning for regression, finding that model-free approaches, in addition to being less computationally intensive to implement, are more effective in improving the performance of linear regressions than model-based alternatives.
Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Macnamee
Activist: A New Framework For Dataset Labelling, Jack O'Neill, Sarah Jane Delany, Brian Macnamee
Conference papers
Acquiring labels for large datasets can be a costly and time-consuming process. This has motivated the development of the semi-supervised learning problem domain, which makes use of unlabelled data — in conjunction with a small amount of labelled data — to infer the correct labels of a partially labelled dataset. Active Learning is one of the most successful approaches to semi-supervised learning, and has been shown to reduce the cost and time taken to produce a fully labelled dataset. In this paper we present Activist; a free, online, state-of-the-art platform which leverages active learning techniques to improve the efficiency …
Bitrate Classification Of Twice-Encoded Audio Using Objective Quality Features, Colm Sloan, Damien Kelly, Naomi Harte, Anil C. Kokaram, Andrew Hines
Bitrate Classification Of Twice-Encoded Audio Using Objective Quality Features, Colm Sloan, Damien Kelly, Naomi Harte, Anil C. Kokaram, Andrew Hines
Conference papers
When a user uploads audio files to a music stream- ing service, these files are subsequently re-encoded to lower bitrates to target different devices, e.g. low bitrate for mobile. To save time and bandwidth uploading files, some users encode their original files using a lossy codec. The metadata for these files cannot always be trusted as users might have encoded their files more than once. Determining the lowest bitrate of the files allows the streaming service to skip the process of encoding the files to bitrates higher than that of the uploaded files, saving on processing and storage space. This …
Modeling Mental Workload Via Rule-Based Expert System: A Comparison With Nasa-Tlx & Workload Profile, Lucas Rizzo, Sarah Jane Delany, Pierpaolo Dondio, Luca Longo
Modeling Mental Workload Via Rule-Based Expert System: A Comparison With Nasa-Tlx & Workload Profile, Lucas Rizzo, Sarah Jane Delany, Pierpaolo Dondio, Luca Longo
Conference papers
In the last few decades several fields have made use of the construct of human mental workload (MWL) for system and task design as well as for assessing human performance. Despite this interest, MWL remains a nebulous concept with multiple definitions and measurement techniques. State-of-the-art models of MWL are usually ad-hoc, considering different pools of pieces of evidence aggregated with different inference strategies. In this paper the aim is to deploy a rule-based expert system as a more structured approach to model and infer MWL. This expert system is built upon a knowledge-base of an expert and transates into computable …
Kicm: A Knowledge-Intensive Context Model, Fredrick Mtenzi, Denis Lupiana
Kicm: A Knowledge-Intensive Context Model, Fredrick Mtenzi, Denis Lupiana
Conference papers
A context model plays a significant role in developing context-aware architectures and consequently on realizing context-awareness, which is important in today's dynamic computing environments. These architectures monitor and analyse their environments to enable context-aware applications to effortlessly and appropriately respond to users' computing needs. These applications make the use of computing devices intuitive and less intrusive. A context model is an abstract and simplified representation of the real world, where the users and their computing devices interact. It is through a context model that knowledge about the real world can be represented in and reasoned by a context-aware architecture. This …