[PDF] Top 20 Adapting machine learning methods to U-statistics
Has 10000 "Adapting machine learning methods to U-statistics" found on our website. Below are the top 20 most common "Adapting machine learning methods to U-statistics".
Adapting machine learning methods to U-statistics
... statistical learning problems, U -statistics are natu- ral estimates of the risk measure one seeks to ...for U -statistic-based ERM, showing that sampling according to the ... Voir le document complet
195
Using Machine Learning Methods to Predict Experimental High Throughput Screening Data
... regression to select the best descriptors in both data sets and create a combined set of explanatory ...considered machine learning methods were then tested on this combined set of variables ... Voir le document complet
20
Adaptive machine learning methods for event related potential-based brain computer interfaces
... allow to spell from a keyboard of flashing ...according to a predefined 63-bit binary sequence (m-sequence), circular-shifted by a different time ...respect to the ... Voir le document complet
169
Machine learning-based sensor data modeling methods for power transformer PHM
... lead to the nonlinearity of the whole system, and FRA and SCR in this condition cannot find the real locations of fault, and also cannot provide the information about the types of transformers [ 11 ...The ... Voir le document complet
18
Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction
... suggests to use the energy rate ...combined to a mass estimation method to learn the thrust profile minimizing the error between the modeled and ob- served energy ... Voir le document complet
13
Machine learning methods for analysis of metabolic data and metabolic pathway modeling
... Abstract: Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict ... Voir le document complet
17
Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction
... distributions to produce random simulated ...according to its probability to give the aircraft positions measured just after ...assumed to be equal to a standard reference mass for the ... Voir le document complet
13
Structured machine learning methods for microbiology : mass spectrometry and high-throughput sequencing
... used to speed up microbial identification: the average time needed for a reliable identification ranges from 6h to 18h [ 60 ...PCR methods have complemented the biochemical approaches, decreasing ... Voir le document complet
132
Sequence-to-sequence learning for machine translation and automatic differentiation for machine learning software tools
... language to be purely functional therefore al- lows us to implement more robust AD and more advanced optimizations compared to imperative ...Similarly to, ...them to Myia as ...together ... Voir le document complet
180
Using Machine Learning to Infer Constraints for Product Lines
... strategies to address various kinds of ...used to produce interactions between T acti- vated features [12, 16, ...ability to detect fault. On the other hand, our basic strategy allows us to ... Voir le document complet
11
Spatial machine learning applied to multivariate and multimodal images
... associating to each observation, which are pixels, a class that is not ...thanks to the acquisition of several hundred narrow spectral ...research to focus on spectral information without considering ... Voir le document complet
197
Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data
... This learning task presents several challenging ...mass to charge channels, and although they are generally pre-processed in order to extract their predominant peaks (Coombes et ...leads to ... Voir le document complet
13
Machine learning: A primer
... unsupervised learning methods—for example, clustering and principal component analysis— as well as supervised learning methods such as regression and ...data to make inferences. This ... Voir le document complet
7
Coloring Panchromatic Nighttime Satellite Images: Comparing the Performance of Several Machine Learning Methods
... Due to the outlier analysis performed, we settled the stretching penalty at a zero ...level. To prevent overfitting, the number of nodes was also fixed at a level of 144 (12x12), which is about 5-50 times ... Voir le document complet
69
Adapting Batch Scheduling to Workload Characteristics: What can we expect From Online Learning?
... backfilling to counter the almost systematic overestimation of the execution time of submitted ...approach to tune EASY ...attempted to use machine learning techniques to predict ... Voir le document complet
11
Monte Carlo Methods in Statistics
... particle learning about the target and sequential improvement of the proposal (Douc et ...applying to any target (instead of a specialised version as for the Gibbs sampler) must be borne in ... Voir le document complet
5
Applying machine learning to event data in soccer
... models to simulate game actions, and predict the outcomes of ...attempted to predict the outcome of matches by using possession rates of different teams and other historical statistics, respectively, ... Voir le document complet
70
Detecting food safety risks and human tracking using interpretable machine learning methods/
... techniques to detect suspected human trafficking ...advertisements to train their deep multimodal network model, named Human Trafficking Deep Network ...simplistic methods, it does not allow for any ... Voir le document complet
80
Empirical Bernstein Inequalities for U-Statistics
... the machine learning commu- nity for learning tasks that are richer than now well-studied classification and ...in learning a ranking function capable of predicting an accurate ordering of ... Voir le document complet
10
Machine learning methods for brain tumor segmentation
... likely to loose their lives due to primary malignant and cen- tral nervous system brain tumor every ...year. To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI ... Voir le document complet
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