Data Mining Practical Machine Learning Tools and Techniques

By อนิรุธ สืบสิงห์. 2014

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Citations in the corpus

A Survey of Machine Learning for Big Code and Naturalness (2017, background): “g the output, since the quality of the output is rarely quantiﬁable. A vast literature on non-probabilistic methods exploits data mining methods, such as frequent pattern mining and anomaly detection [190]. We do not discuss these models here, since they are not probabilistic models of code. Classic”
DeepXplore: Automated Whitebox Testing of Deep Learning Systems (2017, background): “Traditional practices in evaluating machine learning systems primarily measure their accuracy on randomly drawn test inputs from manually labeled datasets [81].”
Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variables (2017, background): “…e.g., Jain et al., 1996) and natural neurons (see, e.g., Marreiros et al., 2008), cumulative distribution functions of usual probability density functions (see, e.g., Waissi and Rossin, 1996), logistic regression (see, e.g., Hilbe, 2009) and machine learning (see, e.g., Witten et al., 2016), etc.”
SemEval-2019 Task 4: Hyperpartisan News Detection (2019, methodology): “For the decision tree, we used the J48 implementation of WEKA (Frank et al., 2016).”
DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars (2017, background): “Traditional practices in evaluating machine learning systems primarily measure their accuracy on randomly drawn test inputs from manually labeled datasets and ad hoc simulations [13, 20, 81].”
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems (2017, methodology): “Most existing testing methods for ML systems involve measuring the accuracy and loss using manually-labeled randomlychosen test samples [52].”
Machine Learning from Theory to Algorithms: An Overview (2018, background): “Unlike other machine learning techniques which learn a single hypothesis from the training data, ensemble learning tries to learn by constructing a set of hypotheses from the training data and by combining them to make a prediction model [15,19] in order to decrease bias(boosting), variance (bagging”
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, background): “Witten et al. (2011) found that ANN models are comparable to SVR models because they can learn to ignore irrelevant attributes.”
Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices (2019, methodology): “This is a more complicated task than simply signaling a microservice with many enqueued requests, for which simpler classification, regression, or sequence labeling techniques would suffice [15, 16, 92].”