Learner corpora and natural language processing
https://doi.org/10.1017/cbo9781139649414.024
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https://doi.org/10.1017/cbo9781139649414.024
https://doi.org/10.1016/0004-3702(93)90017-6
Any book on operationalizing Artificial Intelligence today cannot ignore growth in Natural Language Processing (NLP), with market size projected to be $43b in 2025. Ostensibly the branch of AI concerned with programming computers to underst...
https://doi.org/10.1007/978-1-4842-8817-7_10
https://doi.org/10.1016/0888-613x(87)90007-7
https://doi.org/10.1016/b978-012161964-0/50010-8
Neural Network is defined as the ability of a group to solve more problems than its individual members.The idea brings that a group of people can solve problems efficiently and offer greater insight and a better answer than any one individu...
https://doi.org/10.4018/978-1-59140-176-6
Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g.,...
https://doi.org/10.1109/tetci.2021.3100641
Artificial neural networks are finding many uses in the medical diagnosis application. The goal of this paper is to evaluate artificial neural network in disease diagnosis. Two cases are studied. The first one is acute nephritis disease; da...
Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical de...
https://doi.org/10.1177/0272989x9601600409
This book introduces neural networks. It describes what they are, what they can do and how they do it. While some scientific background is assumed, the reader is not expected to have any prior knowled...
https://doi.org/10.1007/978-1-349-13530-1
Part 1 Fundamentals: basics of neuroscience and artificial neuron models graphs algorithms. Part 2 Feedforward networks: perceptrons and LMS algorithm complexity of learning using feedforward networks adaptive structure networks. Part 3 Rec...
https://doi.org/10.1007/978-1-4615-5451-6_4
https://doi.org/10.1007/978-1-4615-2736-7_9
https://doi.org/10.1016/b978-0-12-083030-5.50007-2
This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and ar...
https://doi.org/10.1016/j.heliyon.2018.e00938
PMC: 6260436
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand...
https://doi.org/10.48550/arxiv.1808.04260
https://doi.org/10.1016/s0167-739x(97)00022-8
It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery ...
https://doi.org/10.1109/36.58972
Abstract Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the pa...
https://doi.org/10.1175/mwr-d-18-0187.1
https://doi.org/10.1007/978-3-642-21004-4_12