Machine Learning
Top PDF Machine Learning:
Machine Learning
... ways. Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other ...Therefore, Machine Learning has become an essential part of modern ...
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Advanced Machine Learning Approach: Deep Learning
... deep learning is that the two different things are not categorized by using structured / labeled ...deep learning neural networks sends the input (image information) through entirely different layers of the ...
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Waffles: A Machine Learning Toolkit
... We present a breadth-oriented collection of cross-platform command-line tools for researchers in machine learning called Waffles. The Waffles tools are designed to offer a broad spectrum of func- tionality ...
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Machine Learning with Operational Costs
... Our main point in this subsection is that one can potentially derive a very wide range of un- certainty sets for robust optimization using different loss functions from machine learning. Box constraints and ...
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Machine Learning in an Auction Environment
... of learning about the eCPMs of ads with uncertain eCPMs in a setting where there is discounting in payoffs as well as random variation in the quality of the competition that an ad faces from competing ads in the ...
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mlr: Machine Learning in R
... the functionality of learners through a wrapper mechanism. Queryable properties provide a reflection mechanism for machine learning objects. Finally, mlr provides sophisticated vi- sualization methods that ...
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Machine Learning in Computer Forensics (and the Lessons Learned from Machine Learning in Computer Security)
... A second point that can be considered is that computer forensics experts typically employ several similar tools to perform the same analysis. This basically happens because not always different tools produce the same ...
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A Machine Learning Approach to the Automatic Evaluation of Machine Translation
... a machine learning approach to evaluating the well- formedness of output of a machine translation system, using classifiers that learn to distinguish human reference translations from machine ...
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Machine Learning and Deep Learning Methods for Cybersecurity
... KNN algorithm is a machine-learning algorithm used for classification, regression. KNN is a non-parametric, lazy learning algorithm. It predicts the classification of a new sample point by using a ...
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Deep Learning as a Frontier of Machine Learning: A Review
... Deep learning, a family of machine learning algorithms, is inspired by the biological process of neural networks is dominating in many applications and proving its advantage over conventional ...
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A machine learning approach
... of machine learning methods in real applications is the poor comprehension of the models produced by these ...of machine learning techniques, it is valuable to work with a model that can be ...
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Machine Learning Algorithms: A Review
... Machine Learning is presently most popular and complex computer vision approach that became most prominent in the research and ...prediction. Machine learning is based on making a system so ...
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Machine-learning the string landscape
... In some sense, this is not surprising, there is underlying struc- ture to any classification problem in our context, which may not be manifest. Indeed, what is novel is to look at the likes of a CICY or a quiver theory as ...
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Machine learning CICY threefolds
... This paper is based on the Masters of Physics project of KB with YHH, both of whom would like to thank the Rudolf Peierls Centre for Theoretical Physics, University of Oxford for the provision of re- sources. YHH would ...
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Machine Learning the Cryptocurrency Market
... Machine learning and AI-assisted trading have attracted growing interest for the past few ...state-of-the-art machine learning algorithms outperform standard ...
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A Brief Study on Machine Learning
... reinforcement machine learning is concerned with how software take actions in an environment so as to maximize a part of cumulative ...in machine learning. Many reinforcement learning ...
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Machine Learning in Compiler Optimization
... Park et al. present a unique graph-based approach for feature representations [121]. They use a SVM where the kernel is based on a graph similarity metric. Their technique requires hand coded features at the basic block ...
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Towards Anthropomorphic Machine Learning
... Last, but not least, human brain is remarkably efficient from the energy consumption point of view. It coordinates a large number of extremely complex tasks all day long. It consumes around 12 watts—a fifth of the power ...
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Arguments in Interactive Machine Learning
... When we first presented the ABML idea [14], the argu- ments were only used to explain learning examples. In one of the following experiments [13], we defined the ABML refinement loop, where arguments turned out to ...
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Persistent Homology and Machine Learning
... The final direction we consider is to encode topological constraints in machine learning algorithms. In [29] topo- logical priors were used to aid in parameter selection. For example, the reconstruction of ...
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