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https://www.aaai.org/Papers/AAAI/2005/SA05-010.pdf
for learning exact SVMs from distributed data sources. Support Vector Machine Algorithm Support Vector Machines (SVM) algorithm (Burges 1998) has been shown to be one of the most effective machine learning algorithms. It gives very good results in terms of accuracy when the data are linearly or non-linearly separa-ble.
https://www.academia.edu/2791965/Learning_support_vector_machines_from_distributed_data_sources
Abstract In this paper we address the problem of learning Support Vector Machine (SVM) classifiers from distributed data sources. We identify sufficient statistics for learning SVMs and present an algorithm that learns SVMs from distributed data by
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.89.7886
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper we address the problem of learning Support Vector Machine (SVM) classifiers from distributed data sources. We identify sufficient statistics for learning SVMs and present an algorithm that learns SVMs from distributed data by iteratively computing the set of sufficient statistics.
https://www.sciencedirect.com/science/article/pii/S187705091501827X
For example, in distributed music classification over P2P networks [8], each peer has access to its own database of songs. Learning in an HP setting has obtained a large amount of attention recently, including distributed versions of support vector machines [9], neural networks [7], and boosting [10].Cited by: 6
https://core.ac.uk/display/24672554
Abstract. In this paper we address the problem of learning Support Vector Machine (SVM) classifiers from distributed data sources. We identify sufficient statistics for learning SVMs and present an algorithm that learns SVMs from distributed data by iteratively computing the set of sufficient statistics.
https://machinelearningmastery.com/support-vector-machines-for-machine-learning/
Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high-performing algorithm with little tuning. In this post you will discover the Support Vector Machine (SVM) machine …
https://faculty.ist.psu.edu/vhonavar/Papers/caragea-thesis.pdf
1.4 Learning from semantically heterogeneous distributed data: each data source has an associated ontology and the user provides a global on- tology and mappings from the …
https://link.springer.com/chapter/10.1007/978-3-319-41706-6_5
Jul 03, 2016 · Support Vector Machines (SVM) have a strong theoretical foundation and a wide variety of applications. ... Caragea, C., Caragea, D., Honavar, V.: Learning support vector machines from distributed data sources. In: Proceedings of the 20th National Conference on Artificial Intelligence (AAAI), vol. 4, pp. 1602–1603. ... Making large-scale ...Cited by: 4
https://docs.microsoft.com/en-us/azure/machine-learning/studio/import-data
Import your training data into Azure Machine Learning Studio (classic) from various data sources. 02/01/2019; 13 minutes to read +7; In this article. To use your own data in Machine Learning Studio (classic) to develop and train a predictive analytics solution, you can use data from:
https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47
Jun 07, 2018 · If not, I suggest you have a look at them before moving on to support vector machine. Support vector machine is another simple algorithm that every machine learning expert should have in his/her arsenal. Support vector machine is highly preferred by many as it produces significant accuracy with less computation power.Author: Rohith Gandhi
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