Incremental predictive clustering trees for online semi-supervised multi-target regression

Aljaž Osojnik; Panče Panov; Sašo Džeroski

doi:10.1007/s10994-020-05918-z

Incremental predictive clustering trees for online semi-supervised multi-target regression

Machine Learning ◽

10.1007/s10994-020-05918-z ◽

2020 ◽

Vol 109 (11) ◽

pp. 2121-2139

Author(s):

Aljaž Osojnik ◽

Panče Panov ◽

Sašo Džeroski

Keyword(s):

State Of The Art ◽

Predictive Performance ◽

The State ◽

High Frequencies ◽

Comparable Performance ◽

Tree Methods ◽

Online Setting ◽

Computational Resources ◽

Good Predictive Performance ◽

Predictive Clustering Trees

Abstract In many application settings, labeling data examples is a costly endeavor, while unlabeled examples are abundant and cheap to produce. Labeling examples can be particularly problematic in an online setting, where there can be arbitrarily many examples that arrive at high frequencies. It is also problematic when we need to predict complex values (e.g., multiple real values), a task that has started receiving considerable attention, but mostly in the batch setting. In this paper, we propose a method for online semi-supervised multi-target regression. It is based on incremental trees for multi-target regression and the predictive clustering framework. Furthermore, it utilizes unlabeled examples to improve its predictive performance as compared to using just the labeled examples. We compare the proposed iSOUP-PCT method with supervised tree methods, which do not use unlabeled examples, and to an oracle method, which uses unlabeled examples as though they were labeled. Additionally, we compare the proposed method to the available state-of-the-art methods. The method achieves good predictive performance on account of increased consumption of computational resources as compared to its supervised variant. The proposed method also beats the state-of-the-art in the case of very few labeled examples in terms of performance, while achieving comparable performance when the labeled examples are more common.

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Accelerating Mobile-Cloud Computing

Cloud Technology ◽

10.4018/978-1-4666-6539-2.ch090 ◽

2015 ◽

pp. 1933-1955

Author(s):

Tolga Soyata ◽

He Ba ◽

Wendi Heinzelman ◽

Minseok Kwon ◽

Jiye Shi

Keyword(s):

Cloud Computing ◽

Mobile Devices ◽

High Speed ◽

Mobile Cloud Computing ◽

Response Times ◽

State Of The Art ◽

The State ◽

Mobile Cloud ◽

Computational Resources ◽

Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today's mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

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Design and evaluation of a biologically-inspired cloud elasticity framework

Cluster Computing ◽

10.1007/s10586-020-03073-7 ◽

2020 ◽

Vol 23 (4) ◽

pp. 3095-3117

Author(s):

Amjad Ullah ◽

Jingpeng Li ◽

Amir Hussain

Keyword(s):

Fuzzy System ◽

State Of The Art ◽

The State ◽

Effective Management ◽

Biologically Inspired ◽

Computational Overhead ◽

Cloud Elasticity ◽

Computational Resources ◽

Auto Scaling ◽

Selection Of

Abstract The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. The existing methods suffer from issues like: (1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; (2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and (3) the lack of considering uncertainty aspects while designing auto-scaling solutions. In this paper, we aim to address these issues using a holistic biologically-inspired feedback switch controller. This method utilises multiple controllers and a switching mechanism, implemented using fuzzy system, that realises the selection of suitable controller at runtime. The fuzzy system also facilitates the design of qualitative elasticity rules. Furthermore, to improve the possibility of avoiding the oscillatory behaviour (a problem commonly associated with switch methodologies), this paper integrates a biologically-inspired computational model of action selection. Lastly, we identify seven different kinds of real workload patterns and utilise them to evaluate the performance of the proposed method against the state-of-the-art approaches. The obtained computational results demonstrate that the proposed method results in achieving better performance without incurring any additional cost in comparison to the state-of-the-art approaches.

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A Survey of Collaborative Filtering Techniques

Advances in Artificial Intelligence ◽

10.1155/2009/421425 ◽

2009 ◽

Vol 2009 ◽

pp. 1-19 ◽

Cited By ~ 1392

Author(s):

Xiaoyuan Su ◽

Taghi M. Khoshgoftaar

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Privacy Protection ◽

State Of The Art ◽

Predictive Performance ◽

The State ◽

Data Sparsity ◽

Research And Practice ◽

Model Based ◽

Comprehensive Survey

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

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A Simplistic Approach for Lightweight Multi-Agent SLAM Algorithm

International Journal of Embedded and Real-Time Communication Systems ◽

10.4018/ijertcs.2020070104 ◽

2020 ◽

Vol 11 (3) ◽

pp. 67-83

Author(s):

Anton Filatov ◽

Kirill Krinkin

Keyword(s):

State Of The Art ◽

Simultaneous Localization And Mapping ◽

The State ◽

Limited Resources ◽

Challenging Problem ◽

Multiple Agents ◽

Localization And Mapping ◽

Multi Agent ◽

Slam Algorithm ◽

Computational Resources

Limitation of computational resources is a challenging problem for moving agents that launch such algorithms as simultaneous localization and mapping (SLAM). To increase the accuracy on limited resources one may add more computing agents that might explore the environment quicker than one and thus to decrease the load of each agent. In this article, the state-of-the-art in multi-agent SLAM algorithms is presented, and an approach that extends laser 2D single hypothesis SLAM for multiple agents is introduced. The article contains a description of problems that are faced in front of a developer of such approach including questions about map merging, relative pose calculation, and roles of agents.

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Accelerating Mobile-Cloud Computing

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Communication Infrastructures for Cloud Computing ◽

10.4018/978-1-4666-4522-6.ch008 ◽

2013 ◽

pp. 175-197 ◽

Cited By ~ 9

Author(s):

Tolga Soyata ◽

He Ba ◽

Wendi Heinzelman ◽

Minseok Kwon ◽

Jiye Shi

Keyword(s):

Cloud Computing ◽

Mobile Devices ◽

High Speed ◽

Mobile Cloud Computing ◽

Response Times ◽

State Of The Art ◽

The State ◽

Mobile Cloud ◽

Computational Resources ◽

Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today’s mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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