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RL algorithms are based on the basic idea of learning via interaction.
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Motivated by human learning, the basic idea of iterative learning control is to use information from previous execution of a trial in order to improve performance from trial to trial.
The basic idea of deep learning is that, for a system S with N level (S1, S2… SN).
The basic idea of dictionary learning algorithms is to approximate training samples as a sparse linear combination of the few dictionary elements.
Thus, the basic idea of supervised learning is to classify data in formal categories that an algorithm is trained to recognize.
The basic idea of unsupervised learning algorithm is to manually assign a probability to each possible context and to use a pre-defined stochastic model to update these likelihoods on the basis of both new sensor readings and the known state of the system (see [42]).
The basic idea of LOR is to "learn" a rank function instead of traditional regression or classification function to predict the activity of candidate compounds for the query target.
The basic idea of the proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate the unknown nonlinear responses.
The basic idea of the original martingale is to directly learn a statistical regularity from already observed data, and then detect possible change(s) by investigating how much each data is deviated from the regularity using martingale by testing exchangeability.
The basic idea of our GRN inference procedure is to learn for each target gene i a model f i in the form of a decision tree (or an ensemble of decision trees), which predicts the promoter state μ i at any time t from the expression levels of the candidate regulators at the same time t.
The basic idea of this approach is to map each variable-length peptide into a fixed-length feature vector such that standard machine learning algorithms are applicable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com