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 ...
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﹍...
Precision Positive predictive value, meaning the fraction of predicted true positive results among all predicted positive results Look for higher precision values. A larger number of false positive predictions lowers the precision of a model. Recall True positive rate (sensitivity), meaning the fraction...
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 ...
The value is still maximized, meaning that the calculation for the class that results in the largest value is taken as the prediction. This is a common implementation simplification as we are often more interested in the class prediction rather than the probability. The input variables are ...
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 ...
If PB(t-l) > 0 and if A and B are not already coupled, then the TCO would produce the following new classifiers: A' = aaaa, aaaa/mmmm B' = bbbb, mmmm/yyyy, where mmmm is a random string with no predefined meaning. The result is a pair of coupled classifiers constructed so that...
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. ...
parameter tuning, limiting their generalizability and interpretability on new datasets [10]. 4. Temporal dependencies pose challenges despite mature solutions in automated machine learning for structured data [11]. This paper proposes a methodology based on prior knowledge of ECG time series shapes, ...
(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...