Titlebook: Discovery Science; 20th International C Akihiro Yamamoto,Takuya Kida,Tetsuji Kuboyama Conference proceedings 2017 Springer International Pu

PARA

Improving Classification Accuracy by Means of the Sliding Window Method in Consistency-Based Featurence to the class label, is the bayesian risk, which represents the theoretical upper error bound of deterministic classification. Experiments reveal . is more accurate than most of the state-of-the-art feature selection algorithms.

anniversary

archetype

Glycogen

configuration

A New Adaptive Learning Algorithm and Its Application to Online Malware Detection approach towards malware detection. To address this problem, machine learning methods have become an attractive and almost imperative solution. In most of the previous work, the application of machine learning to this problem is batch learning. Due to its fixed setting during the learning phase, ba

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季雨

CRUMB

Evaluation of Different Heuristics for Accommodating Asymmetric Loss Functions in Regression problem domains require loss functions that are asymmetric in the sense that the costs for over- or under-predicting the target value may differ. This paper discusses theoretical foundations of handling asymmetric loss functions, and describes and evaluates simple methods which might be used to off

Morphine

Differentially Private Empirical Risk Minimization with Input Perturbationata contributors submit their private data to a database expecting that the database invokes a differentially private mechanism for publication of the learned model. In input perturbation, each data contributor independently randomizes her/his data by itself and submits the perturbed data to the dat

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