2019
AAAI
AAAI 2019
Binary Classifier Inspired by Quantum Theory
Abstract
Abstract Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probability and statistics, which can now be replaced by Quantum Theory (QT) with the aim of improving the effectiveness of ML. In this paper, we propose the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which outperforms the state of the art classification in terms of recall for every category.
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Conference Pioneer
— AAAI 2019
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning
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Trend Setter
— Quantum Computing
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Keyword Pioneer
— quantum theory
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Hot Topic Early Bird
— quantum computing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio