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High volume SVM (machine learning) system

By : user3851801
Date : October 15 2020, 11:12 PM
wish of those help I work on Pattern Recognition problems. Let me please to give you some advices if you plan to work effectively on SVM/ANN problems and if you realy don't have access to a computer cluster:
1) Don't use Matlab. Use Python and its large number of numerical libraries instead for Visualisation/Analysis of your computations.
code :

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Machine learning code dlib with high dimensional labels

By : Adrian
Date : March 29 2020, 07:55 AM
I hope this helps you . Unless there is some special structure between different label types the right thing to do is to train separate models for each label.
However, if there is some special structure to your labels then you can use the structural prediction tools in dlib to handle that. This kind of approach can handle cases where the number of possible labels is very large (e.g. 10^100000, or even infinite). But since you said n

Machine Learning: Is it better to retrain a model if the loss stagnates at a high value?

By : Mohamed A. Sief
Date : March 29 2020, 07:55 AM
I wish this help you In the strict sense, you don't have to retrain as you can continue training with a lower learning rate (this is called a learning shedule). A very common approach is to lower the learning rate (by usually dividing by 10) each time the loss stagnates or becomes constant.
Another approach is to use an optimizer that scales the learning rate with the gradient magnitude, so the learning rate naturally decays as you get closer to the minima. Examples of this are ADAM, Adagrad and RMSProp.

Which machine learning algorithm to use for high dimensional matching?

By : Jitu
Date : March 29 2020, 07:55 AM
it helps some times You basically after some kind of K Nearest Neighbors Algorithm.
Since your data has high dimension you should explore the following:

Train program to understand high and low value in machine learning

By : Greendeviate
Date : March 29 2020, 07:55 AM
around this issue This is the classification problem. You can use classic logistic regression algorithm to classify any given sample into either high value, low value or normal value.
Quoting from the Wikipedia,

Constant Validation Accuracy with a high loss in machine learning

By : user31173
Date : March 29 2020, 07:55 AM
wish helps you I think you have too low learning rate and too few epochs. try with lr = 0.001 and epochs = 100.
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