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Deep imitation learning for 3D navigation tasks. (2017)
Journal Article
HUSSEIN, A., ELYAN, E., GABER, M.M. and JAYNE, C. 2018. Deep imitation learning for 3D navigation tasks. Neural computing and applications [online], 29(7), pages 389-404. Available from: https://doi.org/10.1007/s00521-017-3241-z

Deep learning techniques have shown success in learning from raw high dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imita... Read More about Deep imitation learning for 3D navigation tasks..

Deep reward shaping from demonstrations. (2017)
Conference Proceeding
HUSSEIN, A., ELYAN, E., GABER, M.M. and JAYNE, C. 2017. Deep reward shaping from demonstrations. In Proceedings of the 2017 International joint conference on neural networks (IJCNN 2017), 14-19 May 2017, Anchorage, USA. Piscataway: IEEE [online], article number 7965896, pages 510-517. Available from: https://doi.org/10.1109/IJCNN.2017.7965896

Deep reinforcement learning is rapidly gaining attention due to recent successes in a variety of problems. The combination of deep learning and reinforcement learning allows for a generic learning process that does not consider specific knowledge of... Read More about Deep reward shaping from demonstrations..

Imitation learning: a survey of learning methods. (2017)
Journal Article
HUSSEIN, A., GABER, M.M., ELYAN, E. and JAYNE, C. 2017. Imitation learning: a survey of learning methods. ACM computing surveys [online], 50(2), article 21. Available from: https://doi.org/10.1145/3054912

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has be... Read More about Imitation learning: a survey of learning methods..

An outlier ranking tree selection approach to extreme pruning of random forests. (2016)
Conference Proceeding
FAWAGREH, K., GABER, M.M. and ELYAN, E. 2016. An outlier ranking tree selection approach to extreme pruning of random forests. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 267-282. Available from: https://doi.org/10.1007/978-3-319-44188-7_20

Random Forest (RF) is an ensemble classification technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still ro... Read More about An outlier ranking tree selection approach to extreme pruning of random forests..

Deep active learning for autonomous navigation. (2016)
Conference Proceeding
HUSSEIN, A., GABER, M.M. and ELYAN, E. 2016. Deep active learning for autonomous navigation. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 3-17. Available from: https://doi.org/10.1007/978-3-319-44188-7_1

Imitation learning refers to an agent's ability to mimic a desired behavior by learning from observations. A major challenge facing learning from demonstrations is to represent the demonstrations in a manner that is adequate for learning and efficien... Read More about Deep active learning for autonomous navigation..

A genetic algorithm approach to optimising random forests applied to class engineered data. (2016)
Journal Article
ELYAN, E. and GABER, M.M. 2017. A genetic algorithm approach to optimising random forests applied to class engineered data. Information sciences [online], 384, pages 220-234. Available from: https://doi.org/10.1016/j.ins.2016.08.007

In numerous applications and especially in the life science domain, examples are labelled at a higher level of granularity. For example, binary classification is dominant in many of these datasets, with the positive class denoting the existence of a... Read More about A genetic algorithm approach to optimising random forests applied to class engineered data..

A fine-grained Random Forests using class decomposition: an application to medical diagnosis. (2015)
Journal Article
ELYAN, E. and GABER, M.M. 2015. A fine-grained Random Forests using class decomposition: an application to medical diagnosis. Neural computing and applications [online], 27(8), pages 2279-2288. Available from: https://doi.org/10.1007/s00521-015-2064-z

Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, espe... Read More about A fine-grained Random Forests using class decomposition: an application to medical diagnosis..

On the relationship between variational level set-based and SOM-based active contours. (2015)
Journal Article
ABDELSAMEA, M.M., GNECCO, G., GABER, M.M. and ELYAN, E. 2015. On the relationship between variational level set-based and SOM-based active contours. Computational intelligence and neuroscience [online], 2015, article ID 109029. Available from:https://doi.org/10.1155/2015/109029

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods hav... Read More about On the relationship between variational level set-based and SOM-based active contours..

A scalable expressive ensemble learning using random prism: a MapReduce approach. (2015)
Book Chapter
STAHL, F., MAY, D., MILLS, H., BRAMER, M. and GABER, M.M. 2015. A scalable expressive ensemble learning using random prism: a MapReduce approach. In Hameurlain, A., K√ľng, J., Wagner, R., Sakr, S., Wang, L. and Zomaya, A. (eds.) Transactions on large-scale data- and knowledge-centred systems XX: special issue on advanced techniques for big data management. Lecture notes in computer science, 9070. Berlin: Springer [online], pages 90-107. Available from: https://doi.org/10.1007/978-3-662-46703-9_4

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the infl uence of noise in the data, ensemble learners are often ap... Read More about A scalable expressive ensemble learning using random prism: a MapReduce approach..