1,448,229 research outputs found

    Learning to Order Things

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    There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order instances given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a binary preference function indicating whether it is advisable to rank one instance before another. Here we consider an on-line algorithm for learning preference functions that is based on Freund and Schapire's 'Hedge' algorithm. In the second stage, new instances are ordered so as to maximize agreement with the learned preference function. We show that the problem of finding the ordering that agrees best with a learned preference function is NP-complete. Nevertheless, we describe simple greedy algorithms that are guaranteed to find a good approximation. Finally, we show how metasearch can be formulated as an ordering problem, and present experimental results on learning a combination of 'search experts', each of which is a domain-specific query expansion strategy for a web search engine

    Learning How to Demodulate from Few Pilots via Meta-Learning

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    Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training in order to learn a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data

    The Importance of Language, Memory and Bilingualism in Language Acquisition

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    The process of learning to speak (language) is one of the most important things of early childhood. Within some months, children move from saying single words to longer sentences and from a small vocabulary to one that grows by six new words per day. Language is our main, principal mean of communication. Language tools mean a lot, new opportunities for social understanding, for learning about the world, and for sharing experiences, needs and pleasures. On the other hand, in order to understand how we learn, it is first necessary to understand something about how do we think. Without a good memory language learning would simply be impossible and as a result humans development as well. Memory is undoubtedly one of the most important concepts in remembering things, in learning, because, simply, if things are not remembered, learning cannot take place. Keywords: memory, bilingualism, language acquisition, L1, and L2

    Pembelajaran Kontekstual (Contextual Teaching And Learning) pada Bangun Ruang Sisi Datar di Sekolah Dasar

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    Mathematics learning should be more varied methods and strategies in order to optimize the potential of the student. Selection of methods, strategies, and approaches in designing the learning model in order to achieve an active and meaningful learning is a demand that must be met for teacher. Learning of mathematics which enables fun learning and is done naturally, so student can perform directly the things learned. It is appropriate with contextual teaching and learning is a concept that helps teacher relate the contents of the subject matter with the real world so that learning is more meaningful. In geometry content, teacher should be able to use contextual teaching and learning and also give an innovative questions that can improve higher order thinking skills of the students

    A Developmental Approach to Machine Learning?

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    Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order – with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. We propose that the skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines

    Medical students: A reflective journey over time

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    Experiences in medical education have changed significantly in the last two decades. This evolution is apparent in the teaching/ learning environment, types of education resources and learners. In this article two students provide a reflective account of their individual journeys in medical education spanning across 20 years. While some things remain unchanged, significant differences emerge that stress upon the educators and institutions to stay mindful of these transitions in order to develop learning environments that nurture the growth of future physicians.Experiences in medical education have changed significantly in the last two decades. This evolution is apparent in the teaching/ learning environment, types of education resources and learners. In this article two students provide a reflective account of their individual journeys in medical education spanning across 20 years. While some things remain unchanged, significant differences emerge that stress upon the educators and institutions to stay mindful of these transitions in order to develop learning environments that nurture the growth of future physicians

    Development of Multiliteration and Higher Order Thinking Skills Through Integrated Learning

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    This paper aims to describe integrated learning as a learning approach, which is suggested to be applied in order to develop multiliteration and higher order thinking skills. Multiliteration and higher order thinking skills are needed and must be mastered by the community so that they can actively participate in the 21st century civilization. In this context, education or in its operation is learning to be challenged to be able to develop skills. Based on the literature study, it was concluded that to develop multiliteration and higher order thinking skills, one approach that needs to be applied is the integrated learning approach. There are two things that need to be considered in the application of integrated learning. First, the teacher should formulate learning objectives to develop cognitive process skills at levels C2 through C6. Second, in implementing integrated learning, the teacher ideally applies learning models such as: discover, inquiry, problem based learning, project based learning, cooperative learning models, etc
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