This work focuses on machine learning modeling and predictive control of nonlinear processes using noisy data. We use long short‐term memory (LSTM) networks with training data from sensor measurements corrupted by two types of noise: Gaussian and non〨aussian noise, to train the process model ...
Noisy data unnecessarily increases the amount of storage space required and can adversely affect the results of anydata mininganalysis.Statistical analysiscan use information gleaned from historical data to weed out noisy data and facilitate data mining. Machine learning algorithmsare particularly adept at...
A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements This study presents a physics-informed machine learning-based control method for nonlinear dynamic systems with highly noisy measurements. Existing data-dr... M Ma,J Wu,C Post,... ...
len(params_svm)))# make the training-validation splits on the training datakf=KFold(n_splits=k)fold_index=0fortrain_index,validation_indexinkf.split(Xtr_noisy):# now then get the results on all the parameters considered# SVM with the two parametersforg_index,gammainenumerate(params_rbf):...
Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-scale experiments on synthetic datasets, the practical...
Machine learning offers an intriguing alternative to first-principle analysis for discovering new physics from experimental data. However, to date, purely data-driven methods have only proven successful in uncovering physical laws describing simple, low-dimensional systems with low levels of noise. Here...
Incremental learning from noisy data presents dual challenges: that of evaluating multiple hypotheses incrementally and that of distinguishing errors due t... P Laird 被引量: 13发表: 1993年 Trading Off Simplicity and Coverage in Incremental Concept Learning We present HILLARY, an incremental learning ...
Y Wang,EP Lim,SY Hwang - 《Data & Knowledge Engineering》 被引量: 155发表: 2006年 Trends in Interactive Knowledge Discovery for Personalized Medicine: Cognitive Science meets Machine Learning Consequently, a big trend in computer science is to provide efficient, useable and useful computational meth...
Improving Sentence-Level Relation Classification via Machine Reading Comprehension and Reinforcement Learning be divided into two major approaches: (1) Some works adopt multi-instance learning (MIL) for relation classification to reduce the impact of noisy data... B Xu,Z Zhang,X Zhao,... - Pacifi...
LEARNINGNOISEA METHOD of approach is described here in connexion with international telegraph (Morse) code, but not much change is necessary with other systems of substitution ciphers 1 . At this stage no reference is made to a vocabulary or to the syntactic and semantic context 2 ....