Mark Eastwood

发表

Model level combination of tree ensemble hyperboxes via GFMM

Bogdan Gabrys, Mark Eastwood, B. Gabrys, 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

Building well-performing classifier ensembles : model and decision level combination

Mark Eastwood, M. Eastwood, 2010 .

Restricted Boltzmann machines for pre-training deep Gaussian networks

Chrisina Jayne, Mark Eastwood, Chrisina Jayne, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Understanding rail facilities

Mark Eastwood, 2013 .

A Non-sequential Representation of Sequential Data for Churn Prediction

Bogdan Gabrys, Mark Eastwood, 2009, KES.

Evaluation of hyperbox neural network learning for classification

Chrisina Jayne, Mark Eastwood, Chrisina Jayne, 2014, Neurocomputing.

Unifying and Analysing Activities of Daily Living in Extra Care Homes

Bo Tan, Yanguo Jing, Mark Eastwood, 2018, 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).

Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests

Bo Tan, Yanguo Jing, Mark Eastwood, 2019, 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC).

Dual Deep Neural Network approach to matching data in different modes

Chrisina Jayne, Mark Eastwood, Chrisina Jayne, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Stem cells, tissue engineering and the mechanical environment

Mark Eastwood, Catherine Sarraf, 2005 .

Interpretable, Online Soft-Sensors for Process Control

Petr Kadlec, Mark Eastwood, 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

Building Combined Classifiers

Bogdan Gabrys, Mark Eastwood, B. Gabrys, 2008 .

Deep CNNs with Rotational Filters for Rotation Invariant Character Recognition

Chrisina Jayne, Mark Eastwood, Erik Barrow, 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

New multi-cue bioreactor for tissue engineering of tubular cardiovascular samples under physiological conditions.

Mark Eastwood, Andrew D McCulloch, Andrew B Harris, 2004, Tissue engineering.

Selective Dropout for Deep Neural Networks

Chrisina Jayne, Mark Eastwood, Erik Barrow, 2016, ICONIP.

From Sensor Readings to Predictions: On the Process of Developing Practical Soft Sensors

Athanasios Tsakonas, Bogdan Gabrys, Marcin Budka, 2014, IDA.

Deep Dropout Artificial Neural Networks for Recognising Digits and Characters in Natural Images

Chrisina Jayne, Mark Eastwood, Erik Barrow, 2015, ICONIP.

The Dynamics of Negative Correlation Learning

Bogdan Gabrys, Mark Eastwood, B. Gabrys, 2007, J. VLSI Signal Process..

An intelligent well-being monitoring system for residents in extra care homes

Abdul Hamid, Bo Tan, Yanguo Jing, 2017, IML.

Generalised bottom-up pruning: A model level combination of decision trees

Bogdan Gabrys, Mark Eastwood, B. Gabrys, 2012, Expert Syst. Appl..

Force generation of different human cardiac valve interstitial cells: relevance to individual valve function and tissue engineering.

Mark Eastwood, Magdi H Yacoub, Adrian H Chester, 2007, The Journal of heart valve disease.

Quantitative analysis of collagen gel contractile forces generated by dermal fibroblasts and the relationship to cell morphology

Mark Eastwood, Robert A. Brown, M. Eastwood, 1996, Journal of cellular physiology.

Tissue engineering of biological cardiovascular system surrogates.

Andrew D McCulloch, Mark Eastwood, Andrew B Harris, 2002, Heart, lung & circulation.