Statistical learning/machine learning
The committee machine: Computational to statistical gaps in learning a two-layers neural network
... the statistical physics literature, using the adaptive interpolation method of [ 24 , 11 ], that allows to put several of these results on a firm rigorous ...optimal learning error in the above limit of ...
45
Statistical interpretation of machine learning-based feature importance scores for biomarker discovery
... Univariate statistical tests are widely used for biomarker discovery in ...by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the ...
9
Transductive learning for statistical machine translation
... performance. We will show how such corpora can be used to achieve higher translation quality. Even if large amounts of bilingual text are given, the training of the statistical models usually suffers from sparse ...
10
Fully nonlinear statistical and machine-learning approaches for hydrological frequency estimation at ungauged sites.
... the learning parameter , which appears in the LM algorithm weights, the LM algorithm behaves as a gradient descent method for large values of and as the Gauss-Newton method when is close to zero [Ouarda et ...
36
Machine Learning and Statistical Verification for Security
... Introduction models were used for machine learning and the representation of dependencies be- tween the different variables of a system [MR02]. Each of these formalisms has its own advantages and ...
139
Machine Learning and Statistical Decision Making for Green Radio
... CR Learning Algorithms for OSA 76 The average SER obtained with several policies is investigated in ...all learning approaches and round robin-perfect order becomes negligible as they finish to select an ...
197
Scikit-learn: Machine Learning in Python
... Scipy: efficient algorithms for linear algebra, sparse matrix representation, special functions and basic statistical functions. Scipy has bindings for many Fortran-based standard numer- ical packages, such as ...
6
Machine learning and extremes for anomaly detection
... 4.1 What is Anomaly Detection? Anomaly Detection generally consists in assuming that the dataset under study contains a small number of anomalies, generated by distribution models that differ from the one generating the ...
221
Adapting machine learning methods to U-statistics
... of statistical learning problems, U -statistics are natu- ral estimates of the risk measure one seeks to ...using statistical counterparts of the risk based on much less terms (picked randomly by ...
195
Data integration in machine learning
... integrative machine learning principles, particularly for ...kernel learning, and deep neural ...diferent statistical distributions, possess different semantics, and sufer diferent levels of ...
8
Scikit-learn: Machine Learning in Python
... supervised learning, unsupervised learning, model selection ...known machine learning algorithms, while maintaining an easy-to-use interface tightly integrated with the Python ...for ...
7
Machine learning based localization in 5G
... classical learning solution, KNN, and a deep learning solution, MLP NN, to model the mapping between CSI and the location of the ...by statistical experiments and state-of-the-art works which ...
135
Policy evaluation, high-dimension and machine learning
... hand, if many characteristics are suspected to contribute little (the parameter is dense), a Ridge regression would be better. Similarly, when the regression function can be assumed to be piece-wise constant, a random ...
153
Machine Learning an Experimental Science (Revisited)
... In machine learning research, the question is where we should we spend most of our ...hypothesis statistical tests are widely misinterpreted and when correctly interpreted say ...
7
Predicting Aircraft Descent Length with Machine Learning
... B. Regression using Neural Networks (NNet) Artificial neural networks are algorithms inspired from the biological neurons and synaptic links. An artificial neural network is a graph, with vertices (neurons, or units) and ...
9
Applications of machine learning : basketball strategy
... a statistical method that converts a set of n observations with p variables that are potentially correlated into a set of linearly uncorrelated variables named principal components ...
74
Machine learning: A primer
... a learning algorithm is trained on part of the data and subsequently evaluated on remaining test data from independent individuals to obtain prediction performance estimates (50% accuracy corresponds to random ...
7
Statistics versus Machine Learning
... between statistical inference and ML is subject to intense debate [1]—some methods fall squarely into one domain but many are used in both ...both statistical inference to improve sampling but also acts as ...
8
Dictionary-based Learning in MR Fingerprinting: Statistical Learning versus Deep Learning
... [4] Fabian Balsiger, Amaresha Shridhar Konar, Shivaprasad Chikop, Vimal Chandran, Olivier Scheidegger, Sairam Geethanath, and Mauricio Reyes. Magnetic resonance fingerprinting reconstruction via spatiotemporal ...
5
Machine learning applications in drug development
... and Machine Learning (ML) companies and phar- maceutical labs, as well as universities and research centres [18– 20] , slowly bridge the gap in bioinformatics between applied mathematics, computer sciences ...
13