Примеры использования Training set на Английском языке и их переводы на Русский язык
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New laboratories use a training set;
Create a training set and train a classifier.
This set of samples is called the training set.
Similarly, a larger training set tends to decrease variance.
DMHA is used in bodybuilding and weight training setting.
Subdivide the data into a training set and a hold-out validation set. .
Trying to take subset which is not within the training set.
The CD that is included in the training set free, is a computer program.
The variance is an error from sensitivity to small fluctuations in the training set.
For a given training set, there exists many trees that can code it with no error.
Supervised learning involves learning from a training set of data.
High goals of training set high criteria for discipline and those who direct the training. .
Load a dataset and split it into a training set and a test set. .
From the list of the above potential benefits of DMHA, you can see that a lot of those are applicable to the bodybuilding and weight training setting.
This measure is based on the training set, a sample from this unknown probability distribution.
This is because model-free approaches to inference require impractically large training sets if they are to avoid high variance.
After learning a function based on the training set data, that function is validated on a test set of data, data that did not appear in the training set.
These laboratory studies of patients were used as a training set for a neural network.
Bagging(Bootstrap aggregating) was proposed by Leo Breiman in 1994 to improve classification by combining classifications of randomly generated training sets.
For the shadow theatre is necessary to start sound-check and training sets for at least two hours before the speech, time- about an hour.
Glimmer-MG is an extension to GLIMMER that relies mostly on an ab initio approach for gene finding and by using training sets from related organisms.
You can modify the information you see on the display during training, set automatic laps and customize your heart rate and speed zone settings.
Models with low bias are usually more complex(e.g. higher-order regression polynomials),enabling them to represent the training set more accurately.
High-variance learning methods may be able to represent their training set well but are at risk of overfitting to noisy or unrepresentative training data.
In contrast, models with higher bias tend to be relatively simple(low-order or even linear regression polynomials) butmay produce lower variance predictions when applied beyond the training set.
Overfitting is symptomatic of unstable solutions; a small perturbation in the training set data would cause a large variation in the learned function.
Given a standard training set D{\displaystyle D} of size n, bagging generates m new training sets D i{\displaystyle D_{i}}, each of size n′, by sampling from D uniformly and with replacement.
A grid search algorithm must be guided by some performance metric,typically measured by cross-validation on the training set or evaluation on a held-out validation set. .
Prediction for unlabeled inputs, i.e.,those not in the training set, is treated by the application of a similarity function k{\displaystyle k}, called a kernel, between the unlabeled input x′{\displaystyle\mathbf{x'}} and each of the training inputs x i{\displaystyle\mathbf{x}_{i.
The cardiovascular effort to recover from each set serves a function similar to an aerobic exercise, butthis is not the same as saying that a weight training set is itself an aerobic process.