Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Harry Strange"'
Autor:
Karl Barnfather, Harry Strange
Publikováno v:
New Electronics. 53:30-31
While innovation is booming in machine learning companies need to be aware of the importance of effective patent protection, as Karl Barnfather and Harry Strange explain
Autor:
Reyer Zwiggelaar, Harry Strange
Publikováno v:
Intelligent Data Analysis. 19:1213-1232
The need to reduce the dimensionality of a dataset whilst retaining its inherent manifold structure is key to many pattern recognition, machine learning, and computer vision problems. This process is often referred to as manifold learning since the s
Autor:
Harry Strange, Caroline Boggis, Erika R. E. Denton, Reyer Zwiggelaar, Zhili Chen, Arnau Oliver
Publikováno v:
IEEE Transactions on Biomedical Engineering. 62:1203-1214
Goal: The presence of microcalcification clusters is a primary sign of breast cancer; however, it is difficult and time consuming for radiologists to classify microcalcifications as malignant or benign. In this paper, a novel method for the classific
Publikováno v:
Pattern Recognition Letters. 47:157-163
In mammographic imaging, the presence of microcalcifications, small deposits of calcium in the breast, is a primary indicator of breast cancer. However, not all microcalcifications are malignant and their distribution within the breast can be used to
Autor:
Reyer Zwiggelaar, Harry Strange
Publikováno v:
Breast Imaging ISBN: 9783319415451
Digital Mammography / IWDM
Digital Mammography / IWDM
This paper investigates the use of mereotopological barcodes to help non-experts classify microcalcification clusters as either benign or malignant. When compared against classification using the microcalcification cluster segmentation maps, the use
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::675e2a4c39502572a8a96adda37d042d
https://doi.org/10.1007/978-3-319-41546-8_44
https://doi.org/10.1007/978-3-319-41546-8_44
Autor:
Harry Strange, Reyer Zwiggelaar
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 25:471-476
Manifold learning is a powerful tool for reducing the dimensionality of a dataset by finding a low-dimensional embedding that retains important geometric and topological features. In many applications it is desirable to add new samples to a previousl
Autor:
Harry Strange, Reyer Zwiggelaar
The last few years have seen a great increase in the amount of data available to scientists, yet many of the techniques used to analyse this data cannot cope with such large datasets. Therefore, strategies need to be employed as a pre-processing step
Publikováno v:
Functional plant biology : FPB. 42(5)
Wheat (Triticum aestivum L.) grain size and morphology are playing an increasingly important role as agronomic traits. Whole spikes from two disparate strains, the commercial type Capelle and the landrace Indian Shot Wheat, were imaged using a commer
Publikováno v:
Breast Imaging ISBN: 9783319078861
Digital Mammography / IWDM
Digital Mammography / IWDM
In mammographic images, the presence of microcalcification clusters is a primary indicator of breast cancer. However, not all microcalcification clusters are malignant and it is difficult and time consuming for radiologists to discriminate between ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f73a33f3a3bc627231ecb835d21d50e5
https://doi.org/10.1007/978-3-319-07887-8_86
https://doi.org/10.1007/978-3-319-07887-8_86
Autor:
Reyer Zwiggelaar, Harry Strange
Publikováno v:
SpringerBriefs in Computer Science ISBN: 9783319039428
In this chapter the problems of using spectral dimensionality reduction with large scale datasets are outlined along with various solutions to these problems. The computational complexity of various spectral dimensionality reduction algorithms are lo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::abcced254de1a37a2d5449e6859b47c5
https://doi.org/10.1007/978-3-319-03943-5_6
https://doi.org/10.1007/978-3-319-03943-5_6