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Weight Initialization in Neural Networks: A Journey From , I’d like to invite you to join me on an exploration through different approaches to initializing layer weights in neural networks Step-by-step, through various short experiments and thought exercises, we’ll discover why adequate weight initialization is so important in training deep neural nets
Isolation-basedAnomalyDetection Isolation-basedAnomalyDetection Fei Tony Liu and Kai Ming Ting Gippsland School of Information Technology Monash University and Zhi-Hua Zhou National ,
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Precision and recall - Wikipedia Both precision and recall are therefore based on an understanding and measure of relevance Suppose a computer program for recognizing dogs in photographs identifies 8 dogs in a picture containing 12 dogs and some cats Of the 8 identified as dogs, 5 actually are ,
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Kaiming Ting - ACM author profile page Kai Ming Ting, Ye Zhu, Mark Carman, Yue Zhu, Takashi Washio, Zhi-Hua Zhou , Conventional wisdom in machine learning says that all algorithms are expected to follow the trajectory of a learning curve which is often colloquially referred to as `more data the better' We call this `the gravity of learning curve', and it is assumed that no .
UCI Machine Learning Repository: Glass Identification Data Set Glass Identification Data Set Download: Data Folder, Data Set , Kai Ming Ting and Ian H Witten Issues in Stacked Generalization J , [View Context] Christopher J Merz Using Correspondence Analysis to Combine Classifiers Machine Learning, 36 1999 [View Context] Eibe Frank and Ian H Witten Generating Accurate Rule Sets .
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UCI Machine Learning Repository: Horse Colic Data Set Journal of Machine Learning Research, 3 2002 [View Context] Mark A Hall Department of Computer Science Hamilton, NewZealand Correlation-based Feature Selection for Machine Learning Doctor of Philosophy at The University of Waikato 1999 [View Context] Kai Ming Ting and Ian H Witten Issues in Stacked Generalization J Artif Intell R
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Machine Learning 101 | BigML Machine Learning Are you new to Machine Learning? You're not alone In this page you will find a set of useful articles, videos and blog posts from independent experts around the world that will gently introduce you to the basic concepts and techniques of Machine Learning
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Kaiming He - FAIR Xiangyu Zhang, Jianhua Zou, Xiang Ming, Kaiming He, and Jian Sun Computer Vision and Pattern Recognition ( CVPR ), 2015 IEEE Transactions on Pattern Analysis and Machine Intelligence ( TPAMI ), accepted in 2015
Sensitivity and specificity - Wikipedia Sensitivity and specificity are statistical measures of the performance of a binary classification test, also known in statistics as a classification function, that are widely used in medicine: Sensitivity (also called the true positive rate, the recall, or probability of detection in some fields) measures the proportion of actual positives that are correctly identified as such (eg, the .
Isolation Forest download | SourceForge Jul 07, 2014· Download Isolation Forest for free Isolation Forest detects data-anomalies using binary tre Platform: R (r-projectorg) Reference: Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou, “Isolation Forest”, IEEE International Conference on Data Mining 2008 (ICDM 08)
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Isolation Forest - Nanjing University Isolation Forest Fei Tony Liu, Kai Ming Ting Gippsland School of Information Technology Monash University, Victoria, Australia {tonyliu},{kaimingting}@infotechmonasheduau Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210093, China
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A Comparative Study of Cost-Sensitive Boosting Algorithms (0) CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper describes a study of different adaptations of boosting algorithms for cost-sensitive classification The purpose of the study is to improve our understanding of the behavior of various cost-sensitive boosting algorithms and how variations in the boosting procedure affect misclassification cost and high cost .
Receiver operating characteristic - Wikipedia A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at ,
[D] Kaiming He's original residual network results in 2015 , [D] Kaiming He's original residual network results in 2015 have not been reproduced, not even by Kaiming He himself 1 comment
Machine Learning 101 | BigML 10 days can provide enough time to learn the basics of Machine Learning, and even allow a new practitioner to apply some of these skills to their own projects , How Machines Learn (And You Win) , Kai Ming Ting Paper about the state-of-the-art algorithm to detect anomalies: Isolation Forests
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