20 mins of Liblinear20 mins of Liblinear:
'via Blog this'
Stop working (so hard) — I.M.H.O. — MediumStop working (so hard) — I.M.H.O. — Medium: "The idea that, without “hustle,” without throwing away nights and weekends, without putting your life on hold for your work, you’ll somehow be more successful, more productive, is ridiculous to me, yet continues to be pushed by participants in our industry left and right. This is, quite simply, insane.
3.2. Support Vector Machines — scikit-learn 0.13.1 documentation3.2. Support Vector Machines — scikit-learn 0.13.1 documentation: "In problems where it is desired to give more importance to certain classes or certain individual samples keywords class_weight and sample_weight can be used."
LIBSVM FAQLIBSVM FAQ: "Q: Does libsvm have special treatments for linear SVM?
Soft margin classificationSoft margin classification: "The optimization problem is then trading off how fat it can make the margin versus how many points have to be moved around to allow this margin. The margin can be less than 1 for a point by setting , but then one pays a penalty of in the minimization for having done that."
Support vector machine - Wikipedia, the free encyclopediaSupport vector machine - Wikipedia, the free encyclopedia: ". To keep the computational load reasonable, the mappings used by SVM schemes are designed to ensure that dot products may be computed easily in terms of the variables in the original space, by defining them in terms of a kernel function selected to suit the problem."
opencv - How to speed up svm.predict? - Stack Overflowopencv - How to speed up svm.predict? - Stack Overflow: "The prediction algorithm for an SVM takes O(nSV * f) time, where nSV is the number of support vectors and f is the number of features. The number of support vectors can be reduced by training with stronger regularization, i.e. by increasing the hyperparameter C (possibly at a cost in predictive accuracy)."
QuerySet API reference | Django documentation | DjangoQuerySet API reference | Django documentation | Django: "In some complex data-modeling situations, your models might contain a lot of fields, some of which could contain a lot of data (for example, text fields), or require expensive processing to convert them to Python objects. If you are using the results of a queryset in some situation where you know you don’t need those particular fields, you can tell Django not to retrieve them from the database."
Support Vector Machines: ParametersSupport Vector Machines: Parameters: ""However, it is critical here, as in any regularization scheme, that a proper value is chosen for C, the penalty factor. If it is too large, we have a high penalty for nonseparable points and we may store many support vectors and overfit. If it is too small, we may have underfitting."
New HTML Parser: The long-awaited libxml2 based HTML parser code is live. It needs further work but already handles most markup better than the original parser.
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