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Janez Demšar et al. "Orange: data mining toolbox in python". The Journal of Machine Learning Research, January 2013. Marinka Žitnik, Blaž Zupan. "Data fusion by matrix factorization". IEEE Transactions on Pattern Analysis and Machine Intelligence 37(1):41–53, 2015. Marinka Žitnik, Edward E. Nam, Chris Dinh, Adam Kuspa, Gad Shaulsky in ...
It can ingest data from offline data sources (such as Hadoop and flat files) as well as online sources (such as Kafka). Pinot is designed to scale horizontally. Mondrian OLAP server is an open-source OLAP server written in Java. It supports the MDX query language, the XML for Analysis and the olap4j interface specifications.
Let the number of training points be N and the number of features in the training data be D. Let L be the number of individual models in the ensemble. For each individual model l, choose n l (n l < N) to be the number of input points for l. It is common to have only one value of n l for all the individual models.
He was the founding editor-in-chief of the Journal of Computer Security (1992-2010) and a past editor of IEEE Transactions on Computers (2016-2019), ACM Transactions on Information and Systems Security (1999-2006), IET Information Security (2007-2014), International Journal of Cooperative Information Systems (1992-2011), IEEE Concurrency (1997 ...
The method uses scattering parameters of a material sample embedded in a waveguide, namely and , to calculate permittivity and permeability data. and correspond to the cumulative reflection and transmission coefficient of the sample that are referenced to the each sample end, respectively: these parameters account for the multiple internal reflections inside the sample, which is considered to ...
To create a synthetic data point, take the vector between one of those k neighbors, and the current data point. Multiply this vector by a random number x which lies between 0, and 1. Add this to the current data point to create the new, synthetic data point. Many modifications and extensions have been made to the SMOTE method ever since its ...
Mean shift is an application-independent tool suitable for real data analysis. Does not assume any predefined shape on data clusters. It is capable of handling arbitrary feature spaces. The procedure relies on choice of a single parameter: bandwidth. The bandwidth/window size 'h' has a physical meaning, unlike k-means.
The journal covers research in computer vision and image understanding, pattern analysis and recognition, machine intelligence, machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition.