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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Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. Approaches based on deep neural networks have recently proven successful on st...
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In this article we present an application of Kalman filtering in Artificial Intelligence, where nonlinear Kalman filters were used as a learning algorithms for feed-forward neural networks. In the first part of this a...
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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.
This research investigates road sign recognition using deep learning methods, comparing them to traditional approaches and emphasizing the potential of simplified convolutional neural networks. Evaluations were conduc...
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Traffic crashes are the severe issues confronting the world as they are the root reason for numerous deaths, wounds, and fatalities just as financial misfortunes consistently. Effective model to deduce the severity of...
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An approach to the composition of learning algorithms for classes of constant VC-dimension into learning algorithms for more complicated classes is presented. The composition theorem is proven for a broader set of cla...
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An approach to the composition of learning algorithms for classes of constant VC-dimension into learning algorithms for more complicated classes is presented. The composition theorem is proven for a broader set of classes C and for other learning models. It is shown that if a class of concepts C is exactly learnable in time t by a hypothesis class H of constant VC-dimension then the class C* is learnable in time polynomial in t and m. A much weaker condition is also shown that can be placed on C and H to ensure learnability of C* is shown. The composition theorem cannot be extended to classes with nonconstant VC-dimension.
We present discrete stochastic optimization algorithms that adaptively learn the Nernst potential in membrane ion channels. The proposed algorithms dynamically control both the ion channel experiment and the resulting...
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We present discrete stochastic optimization algorithms that adaptively learn the Nernst potential in membrane ion channels. The proposed algorithms dynamically control both the ion channel experiment and the resulting Hidden Markov Model (HMM) signal processor and can adapt to time-varying behaviour of ion channels. One of the most important properties of the proposed algorithms are their its self-learning capability - they spends most of the computational effort at the global optimizer (Nernst potential). Numerical examples illustrate the performance of the algorithms on computer generated synthetic data.
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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