This paper provides an approach to Port2Port Business Process Intelligence (BPIs) helping decision makers in tackling constant changes in governance responsibilities. This consideration leads to the need for Port2Port...
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Innovations in neuro-technology have created a potential gap in our ability to measure human performance and decision making in dynamic environments. Therefore, a need exists to create more reliable testing methodolog...
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Incremental learning refers to learning from streaming data, which arrive over time, with limited memory resources and, ideally, without sacrificing model accuracy. This setting fits different application scenarios su...
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ISBN:
(纸本)9782875870278
Incremental learning refers to learning from streaming data, which arrive over time, with limited memory resources and, ideally, without sacrificing model accuracy. This setting fits different application scenarios such as learning in changing environments, model personalisation, or lifelong learning, and it offers an elegant scheme for big data processing by means of its sequential treatment. In this contribution, we formalise the concept of incremental learning, we discuss particular challenges which arise in this setting, and we give an overview about popular approaches, its theoretical foundations, and applications which emerged in the last years.
We present an approach to the design of an automatic text summarizer that generates a summary by extracting sentence segments. First, sentences are broken into segments by special cue markers. Each segment is represen...
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Deep Reinforcement learning (DRL) is poised to revolutionize the field of AI and represents a step towards general intelligence. Currently, AlphaStar achieved the Grandmaster level in StarCraft gaming, which is a rema...
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Aluminium smelting processes are typically complex and multidisciplinary, hard to be modeled and controlled. Recent advances in technology, such as IIoT and the Industrie 4.0 paradigm, have helped this industry to ove...
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The new algorithm based on network decomposition into layers and estimation of the local weights by using Extended Kalman Filter (EKF) derived from the local optimality criteria is proposed in this paper. Local optima...
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The new algorithm based on network decomposition into layers and estimation of the local weights by using Extended Kalman Filter (EKF) derived from the local optimality criteria is proposed in this paper. Local optimality criteria are formulated on the basis of the a specific output error back-propagation. Simulation examples show a high efficiency of the proposed algorithm from the point of view of both convergence rate and generalization capabilities.
Active learning is a generic approach to accelerate training of classifiers in order to achieve a higher accuracy with a small number of training examples. In the past, simple active learning algorithms like random le...
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ISBN:
(纸本)9781581139648
Active learning is a generic approach to accelerate training of classifiers in order to achieve a higher accuracy with a small number of training examples. In the past, simple active learning algorithms like random learning and query learning have been proposed for the design of support vector machine (SVM) classifiers. In random learning, examples are chosen randomly, while in query learning examples closer to the current separating hyperplane are chosen at each learning step. However, it is observed that a better scheme would be to use random learning in the initial stages (more exploration) and query learning in the final stages (more exploitation) of learning. Here we present two novel active SV learning algorithms which use adaptive mixtures of random and query learning. One of the proposed algorithms is inspired by online decision problems, and involves a hard choice among the pure strategies at each step. The other extends this to soft choices using a mixture of instances recommended by the individual pure strategies. Both strategies handle the exploration- exploitation trade-off in an efficient manner. The efficacy of the algorithms is demonstrated by experiments on benchmark datasets. Copyright 2005 ACM.
Traditionally, data valuation is posed as a problem of equitably splitting the validation performance of a learning algorithm among the training data. As a result, the calculated data values depend on many design choi...
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Due to the various dangers involved, especially tropical cyclones, they are one of the most common and deadly calamities in the world. The very important primary step in monitoring this destructive disaster is by esti...
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