training data

Experimental results show that this method can improve the accuracy of the class with less training data under a high total accuracy.

Each user has at least 10 ratings in the training data, which were given by the user earlier than his validation ratings.

Point training data can be converted to a fuzzy set, therefore the new method can predict both point events and area-events.

Specifies the percentage of cases within the training data used to calculate the holdout error.

If the training data and diagnostic data used in neural network diagnosis have redundant information, it will affect the diagnostic ability.

That won't help us at all in predicting future unknowns, since it's perfectly suited only for our existing training data.

The data set is projected into low dimensional PCA space using PCA, and the low dimensional data set used as training data set of RVM.

Unlike the ordinary weights of evidence using point training sets, the new method involves training data as a fuzzy set.

By reduction processing to the import space, this method adopts artificial neural network for data mining on the reduced training data.

This node cannot be unchecked because the predicted entity must be included in the training data.

The training data is inevitably contaminated with noise. At first the neural network is represented with a probability relation.

Summary and analyze the regional relevant training data, take the improvement action for the weak link.

An anomaly detection model was constructed from the distance in the principal component space and the reconstruction error of training data.

And, it would reduce manual operation greatly to label training data by converting extraction into classification.

Your analysis will focus on the impact of context representation and the features of training data on the quality of generated clusters.

In the scenario of MIL, the labels of individual instances in the training data are not available, instead the bags are labeled.

There exists a basic assumption: the training data and test data are drawn from the same distribution and the same feature space.

However, when noisy training data are present, its performance will decline sharply.

Sample cases may be generated and may not actually exist in the training data.

The case table includes the training data that you will use to train the mining model.

The thesis has made a series of research on training data, evaluation data and acoustic models.

Shen Jiaying and her team is planning to put up an aid site for children's psychology counselling. She is preparing the training data today.

Before the NN training process, the training data is firstly analyzed and name prior knowledge about the problem is obtained.

Finally, discuss and research how to select the number of the neural units and the training data.

It votes the predictive results of basic classifier, and then takes the voting results as training data of level-1.

Removes all training data from a mining structure.

Change the frequency classes in the training data in proportion to their costs.

On the Training Data page, select the Key check boxes next to the TimeIndex and ModelRegion columns.

Keep and track training data.

According to the training data, the path controller of fuzzy neural network is designed based on ANFIS.

First, create a computer program for generating training data of neural network without practical manipulation experience of suspension;

Travel, training, data collation and sorting data and preparing the interview related to recruiting some of the work;

when both evidence and training data are fuzzy sets , the new method acts as a dual fuzzy weights of evidence method

Firstly the training data set is converted to the standard unit features metric space;

Multiresolution Algorithm of Wavelet Network Learning from Absent Training Data

Anomaly Detection Model with Unlabeled Training Data

Design and Application of Management and Analysis System of Diving Training Data

Anomaly Detection Method for Noisy Training Data

Fast Reduction for Large-Scale Training Data Set

New method of SVM learning with large scale training data