The classes removed in the validation phase have both low recall and (relatively) high precision, meaning they are “repeller” classes and so distribute their error across other classes. Combined, these three
If the Cat Detector was working perfectly, then you’d feed it an input image of a cat and it would (correctly) output the labelpositive(meaning that it thinks the input image is indeed, a cat). And if you feed it an input image that’snota cat (like an image of a dog), then ...
and the tree with the highest probability is selected. The number of decision trees can be predefined. Each tree samples a random subset of the input data during training. The meaning of results and the high number of decision trees can prevent overfitting. The parallel training of trees is ...
We propose a novel PDF malware detection method, using active learning to boost training. Particularly, we first make clear the meaning of uncertain samples in this paper, and theoretically explain the effectiveness of these uncertain samples for malware detection. Second, we present an active﹍...
Trainable classifiers use machine learning to detect content based on meaning and context rather than predefined patterns. Unlike sensitive information types (SITs), which rely on keywords or pattern-based detection, trainable classifiers improve classification accuracy by analyzing real-...
(also called numerative), an auxiliary lexeme or noun that has lost its basic meaning to a greater or lesser degree and is used to designate countable objects. Classifiers are used in an attributive word group that contains a numeral and a noun; an example in Russian is piat’ shtuk karan...
The best overall score might not be the best model for your goal. A model with a slightly lower overall accuracy might be the best classifier for your goal. For example, false positives in a particular class might be important to you. You might want to exclude some predictors where data ...
The algorithm cannot access the full feature values in most practical cases in recommendation tasks. Reasons for missing values can be diverse [46], but most likely follow a not missing at random mechanism, meaning that the probability of a missing value depends on the features. To implement ...
At the same time, some SFs may present more than one meaning, being contextually polysemous. While it is unlikely that an organization (except a really large one) would cre- ate this type of expression, it can still be encountered in the extraction process. There may be other cases where...
All the symbols meaning is given in Table 3. Table 3 Meaning of symbols used in Eqs. (1)–(7) Full size table Adam: Adam (short for adaptive moment estimation) is a gradient-based optimization algorithm used in deep learning for updating the weights of a neural network during training. ...