Examples of using Training examples in English and their translations into Chinese
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Political
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Ecclesiastic
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Programming
Luckily, the training data also includes background noise which we mix with our training examples at various volumes.
Thus, we have 3 input nodes to the network and 4 training examples.
In the following plot you can see that the SVM could benefit from more training examples.
This means that given a number of training examples, the system needs to be able to generalize to examples it has never seen before.
The green line also accurately fits the Tokyo training examples, but also generalizes well to Texas, never preferring farther cafes.
In other words, imagine predicting the emotion of a tweet without providing any training examples of tweets with that emotion label.
Depending on the problem you want to solve, we may be talking in the order of hundreds, thousands, millions or billions of training examples.
We can vectorize these steps for‘m' training examples as shown below.
MNIST is composed of handwritten digits and includes 60,000 training examples and 10,000 test examples. .
For example, standard decision tree learners cannot learn trees with more leaves than there are training examples.
With their framework, the robot is given a limited number of training examples, and uses them to generalize to new objects.
A low C value makes the decision surface smooth, while a high C value aims at classifying all training examples correctly.
MNIST is composed of handwritten digits and includes 60,000 training examples and 10,000 test examples. .
That process starts by gathering training examples, which is often the hardest part of the task;
Also available, training examples prepared by the IPC Revision Working Group and PowerPoint presentations addressing different issues relating to the use of the IPC.
If we have training examples and classes then the loss for our prediction with respect to the true labels is given by.
To collect their training examples, the researchers developed a simple application for devices that use Apple's iOS operating system.
Suppose the training examples are points in a room, and the height dimension is not that important for our purposes.
A policy that randomly stumbles onto good training examples will bootstrap itself much faster than a policy that doesn't.
Yes, high bias models will not benefit from more training examples, but they might very well benefit from more features.