15,120 research outputs found
Open-world Learning and Application to Product Classification
Classic supervised learning makes the closed-world assumption, meaning that
classes seen in testing must have been seen in training. However, in the
dynamic world, new or unseen class examples may appear constantly. A model
working in such an environment must be able to reject unseen classes (not seen
or used in training). If enough data is collected for the unseen classes, the
system should incrementally learn to accept/classify them. This learning
paradigm is called open-world learning (OWL). Existing OWL methods all need
some form of re-training to accept or include the new classes in the overall
model. In this paper, we propose a meta-learning approach to the problem. Its
key novelty is that it only needs to train a meta-classifier, which can then
continually accept new classes when they have enough labeled data for the
meta-classifier to use, and also detect/reject future unseen classes. No
re-training of the meta-classifier or a new overall classifier covering all old
and new classes is needed. In testing, the method only uses the examples of the
seen classes (including the newly added classes) on-the-fly for classification
and rejection. Experimental results demonstrate the effectiveness of the new
approach.Comment: accepted by The Web Conference (WWW 2019) Previous title: Learning to
Accept New Classes without Trainin
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
In lifelong learning systems, especially those based on artificial neural
networks, one of the biggest obstacles is the severe inability to retain old
knowledge as new information is encountered. This phenomenon is known as
catastrophic forgetting. In this article, we propose a new kind of
connectionist architecture, the Sequential Neural Coding Network, that is
robust to forgetting when learning from streams of data points and, unlike
networks of today, does not learn via the immensely popular back-propagation of
errors. Grounded in the neurocognitive theory of predictive processing, our
model adapts its synapses in a biologically-plausible fashion, while another,
complementary neural system rapidly learns to direct and control this
cortex-like structure by mimicking the task-executive control functionality of
the basal ganglia. In our experiments, we demonstrate that our self-organizing
system experiences significantly less forgetting as compared to standard neural
models and outperforms a wide swath of previously proposed methods even though
it is trained across task datasets in a stream-like fashion. The promising
performance of our complementary system on benchmarks, e.g., SplitMNIST, Split
Fashion MNIST, and Split NotMNIST, offers evidence that by incorporating
mechanisms prominent in real neuronal systems, such as competition, sparse
activation patterns, and iterative input processing, a new possibility for
tackling the grand challenge of lifelong machine learning opens up.Comment: Key updates including results on standard benchmarks, e.g., split
mnist/fmnist/not-mnist. Task selection/basal ganglia model has been
integrate
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Pedagogic approaches to using technology for learning: literature review
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Load flow studies on stand alone microgrid system in Ranau, Sabah
This paper presents the power flow or load flow analysis of Ranau microgrid, a
standalone microgrid in the district of Ranau,West Coast Division of Sabah. Power
flow for IEEE 9 bus also performed and analyzed. Power flow is define as an
important tool involving numerical analysis applied to power system. Power flow
uses simplified notation such as one line diagram and per-unit system focusing on
voltages, voltage angles, real power and reactive power. To achieved that purpose,
this research is done by analyzing the power flow analysis and calculation of all the
elements in the microgrid such as generators, buses, loads, transformers,
transmission lines using the Power Factory DIGSilent 14 software to calculate the
power flow. After the analysis and calculations, the results were analysed and
compared
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