training data setの例文

• Different snakes will require different training data sets and tunings.
• For each class, k flats are trained a priori via training data set.
• Imagine that we have available several different, but equally good, training data sets.
• The context in line completion is the current line, while current document poses as training data set.
• In the training data set ( 218 units ), the fault grew in magnitude until system failed.
• But if the learning algorithm is too flexible, it will fit each training data set differently, and hence have high variance.
• Known values for the target variable are provided for the training data set and test data set, but should be predicted for other data.
• This is called overfitting, and is particularly likely to happen when the size of the training data set is small, or when the number of parameters in the model is large.
• Since the actual number of total labels is unknown ( from a training data set ), a hidden estimate of the number of labels given by the user is utilized in computations.
• As the size of training data set approaches infinity, the one nearest neighbour classifier guarantees an error rate of no worse than twice the Bayes error rate ( the minimum achievable error rate given the distribution of the data ).
• In some implementations the learning coefficient ? and the neighborhood function ? decrease steadily with increasing s, in others ( in particular those where t scans the training data set ) they decrease in step-wise fashion, once every T steps.
• A machine learning algorithm, also known as a learning map L, maps a training data set, which is a set of labeled examples ( x, y ), onto a function f from X to Y, where X and Y are in the same space of the training examples.
• Also, has any theoretical work been done on how to choose old training data to delete in order to keep the training data set limited in size ( and thus work around the O ( n ^ 3 ) training time complexity ) in cases where the data population is partly evolving and partly constant?
• If, however, you use leave-one-out cross validation in the model fitting phase, the trace of the smoothing matrix is always zero, corresponding to zero parameters for the AIC . Thus, NPMR with cross-validation in the model fitting phase already penalizes the measure of fit, such that the error rate of the training data set is expected to approximate the error rate in a validation data set.
• Three broad categories of anomaly detection techniques exist . "'Unsupervised anomaly detection "'techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set . "'Supervised anomaly detection "'techniques require a data set that has been labeled as " normal " and " abnormal " and involves training a classifier ( the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection ) . "'Semi-supervised anomaly detection "'techniques construct a model representing normal behavior from a given " normal " training data set, and then testing the likelihood of a test instance to be generated by the learnt model.