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Learning algorithms

Investigating Machine Learning Algorithms for Modeling SSD I/O Performance for Container-based Virtualization

Investigating Machine Learning Algorithms for Modeling SSD I/O Performance for Container-based Virtualization

... In what follows, we will describe each of the five ma- chine learning algorithms used and give some elements about hyperparameters configuration. Decision trees (DT) This method was developed at the ...

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Biologically-plausible learning algorithms can scale to large datasets

Biologically-plausible learning algorithms can scale to large datasets

... biologically-plausible algorithms, pro- posed by Liao et ...comparable learning capabilities to that of BP on small ...biologically-plausible learning algorithms on more difficult datasets and ...

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Evaluating product-based possibilistic networks learning algorithms

Evaluating product-based possibilistic networks learning algorithms

... Learning algorithms could be evaluated numerically by comparing the initial network and the learned one using a possibilistic dissimilarity measure between their joint possibility distribution as done by KL ...

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Annotating mobile phone location data with activity purposes using machine learning algorithms

Annotating mobile phone location data with activity purposes using machine learning algorithms

... 1.3. Research contributions Extending the current research on semantic annotation of people’s movement traces, and in the meantime addressing the above mentioned limitations, our study proposes a new approach which is ...

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An Empirical Comparison of Learning Algorithms for Nonparametric Scoring

An Empirical Comparison of Learning Algorithms for Nonparametric Scoring

... modern learning al- gorithms consists of performance maximization (or risk minimization, equivalently) and one ex- pects that the closer the optimization risk func- tional is to the actual target risk functional, ...

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New structure learning algorithms and evaluation methods for large dynamic Bayesian networks

New structure learning algorithms and evaluation methods for large dynamic Bayesian networks

... Generating a very large BN randomly is not very realistic. In many large ap- plications, the global model can be decomposed in coherent repeated subgraphs. In the second familly, they chose the reference model as ...

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Benchmarking dynamic Bayesian network structure learning algorithms

Benchmarking dynamic Bayesian network structure learning algorithms

... structure learning algorithms have been proposed, adapting principles already used in "static" ...these algorithms is a difficult task because the evaluation technique and/or the reference ...

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Semantic annotation of mobile phone data using machine learning algorithms

Semantic annotation of mobile phone data using machine learning algorithms

... machine learning algo- rithms and an ensemble of the above algorithms are employed; an additional enhance- ment algorithm is ...machine learning algorithms and the characteristics of ...

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Improvement of the LPWAN AMI Backhaul's Latency thanks to Reinforcement Learning Algorithms

Improvement of the LPWAN AMI Backhaul's Latency thanks to Reinforcement Learning Algorithms

... Fig. 2 Collision between two packets in the same frequency channel a large number of end-devices use the same SF. This can cause a large number of collisions in the network. As we focus on the problem of collisions in ...

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Advances in scaling deep learning algorithms

Advances in scaling deep learning algorithms

... Deep learning algorithms are a new development in machine ...deep learning may be a key component in learning AI-hard tasks ( Bengio , 2009a ...deep learning is a powerful solution to ...

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Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms

Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms

... In KNN, the training examples are used to form K neighborhood classes. A plurality vote of its neighbors classifies an object. The objective being assigned to the class most common among its k nearest neighbors. k ...

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Biologically-Plausible Learning Algorithms Can Scale to Large Datasets

Biologically-Plausible Learning Algorithms Can Scale to Large Datasets

... weights. We asked whether the same happens in sign-symmetry by computing average alignment angles similar to Lillicrap et al. (2016): For every pair of feedforward and feedback weight matri- ces, we flatten the matrices ...

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Learning Algorithms for Keyphrase Extraction

Learning Algorithms for Keyphrase Extraction

... describe learning algorithms, which can be trained by supplying documents with associated target summaries (Kupiec et ...1997). Learning algorithms can be extended to new domains with less ...

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The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms

The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms

... reinforcement learning, temporal-difference learning, DQV, DQV-Max-Learning ...Reinforcement Learning (RL) the aim is to construct algorithms which learn value functions that are either ...

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Aggregation of Multi-Armed Bandits Learning Algorithms for Opportunistic Spectrum Access

Aggregation of Multi-Armed Bandits Learning Algorithms for Opportunistic Spectrum Access

... bandit algorithms have been recently studied and evaluated for Cognitive Radio (CR), especially in the context of Opportunistic Spectrum Access ...aggregation algorithms can be useful to select on the run ...

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Predicting Bus End-Trip Delays Using Different Machine Learning Algorithms to Model Planning Effectiveness

Predicting Bus End-Trip Delays Using Different Machine Learning Algorithms to Model Planning Effectiveness

... The data from another period (autumn 2016) were then added to the database, and the model tested on the aggregated database. The model accuracy remained constant after the addition of the new period. The models were then ...

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ABR prediction using supervised learning algorithms

ABR prediction using supervised learning algorithms

... ML algorithms presented in section II-B: Logistic Regression (LGS), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Ada Boost (AdBst), Gradient Boost (GrdBst), Naive Bayes (NB), K-Nearest ...

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A Note on the Interpretability of Machine Learning Algorithms

A Note on the Interpretability of Machine Learning Algorithms

... 5.2 Unconditional counterfactual explanation method This approach provides a way to understand how a given decision has been obtained, and can provide grounds to contest it, and advice on how the data input can change ...

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Consumer Credit-Risk Models Via Machine-Learning Algorithms

Consumer Credit-Risk Models Via Machine-Learning Algorithms

... machine learning—to assign weights on features by their importance, however in this study, we focus on the simpler approach of rank-ordering the data by simply counting number of missing values, without ...

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Statistical learning methods for ranking : theory, algorithms and applications

Statistical learning methods for ranking : theory, algorithms and applications

... statistical counterpart which is of the form of a U -statistic. Whereas the theoret- ical properties of decision rules based on optimizing such statistics are becoming well-documented in the machine-learning ...

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