In this work, a new approach lean on minor component analysis (MCA) neural learning and fractional derivative (FD) is introduced for the design of digital finite impulse response (FIR) filters. In this method, design ...
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Artificial intelligence's (AI) rapid evolution has made it a critical technology in medicine, education, research, computer vision, natural language processing, automatic driving, robotics and automation, and othe...
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Due to easy access to the internet, the content on social media increased drastically. It is easy to write or spread anything on the web without taking care of the trustfulness of the source. Fake news is now a whole ...
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Mobile users install different types of applications on their mobile devices based on their interests and needs and perform various activities on them (known as in-app activities). In this paper, we demonstrate that a...
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This paper is focus on the hierarchical optimal multi-control problem with and saturating control inputs, it does not to require any model information for linear systems. To avoid reconstructing the model, a model-fre...
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In this paper, we propose three novel algorithms to help educators generate questions to evaluate learners’ comprehension of the learning material. First, we propose the Automatic Question Generator algorithm that au...
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Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that updates that model when given examples from a new task. This additional level of learning can be ...
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ISBN:
(纸本)9781713829546
Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that updates that model when given examples from a new task. This additional level of learning can be powerful, but it also creates another potential source of overfitting, since we can now overfit in either the model or the base learner. We describe both of these forms of meta-learning overfitting, and demonstrate that they appear experimentally in common meta-learning benchmarks. We introduce an information-theoretic framework of meta-augmentation, whereby adding randomness discourages the base learner and model from learning trivial solutions that do not generalize to new tasks. We demonstrate that meta-augmentation produces large complementary benefits to recently proposed meta-regularization techniques.
Sexism is defined as discrimination among females of all ages. We have seen a rise of sexism in social media platforms manifesting itself in many forms. The paper presents best performing machine learning and deep lea...
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Machine learning (ML) has made significant progress to perform different tasks, such as image classification, speech recognition, and natural language processing, mainly driven by deep learning. Also, ML algorithms, t...
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In computer security Intrusion Detection System (IDS) is a mechanism of detecting an intruder in the system and notifying malicious activities for system administrators. The IDS researches on wireless Local Area Netwo...
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