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You searched for +publisher:"Rutgers University" +contributor:("Awasthi, Pranjal"). Showing records 1 – 4 of 4 total matches.

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Rutgers University

1. Gupta, Mayank, 1990-. A comparison of the triangle algorithm and sequential minimal optimization algorithm for solving the hard margin problem.

Degree: MS, Computer Science, 2016, Rutgers University

 In this article we consider the problem of testing, for two nite sets of points in the Euclidean space, if their convex hulls are disjoint… (more)

Subjects/Keywords: Convex sets

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APA (6th Edition):

Gupta, Mayank, 1. (2016). A comparison of the triangle algorithm and sequential minimal optimization algorithm for solving the hard margin problem. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/49973/

Chicago Manual of Style (16th Edition):

Gupta, Mayank, 1990-. “A comparison of the triangle algorithm and sequential minimal optimization algorithm for solving the hard margin problem.” 2016. Masters Thesis, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/49973/.

MLA Handbook (7th Edition):

Gupta, Mayank, 1990-. “A comparison of the triangle algorithm and sequential minimal optimization algorithm for solving the hard margin problem.” 2016. Web. 31 Oct 2020.

Vancouver:

Gupta, Mayank 1. A comparison of the triangle algorithm and sequential minimal optimization algorithm for solving the hard margin problem. [Internet] [Masters thesis]. Rutgers University; 2016. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49973/.

Council of Science Editors:

Gupta, Mayank 1. A comparison of the triangle algorithm and sequential minimal optimization algorithm for solving the hard margin problem. [Masters Thesis]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49973/


Rutgers University

2. Tonde, Chetan J., 1985-. Supervised feature learning via dependency maximization.

Degree: PhD, Computer Science, 2016, Rutgers University

A key challenge in machine learning is to automatically extract relevant feature representations of data for a given task. This becomes especially formidable task for… (more)

Subjects/Keywords: Machine learning; Kernel functions

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APA (6th Edition):

Tonde, Chetan J., 1. (2016). Supervised feature learning via dependency maximization. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/50200/

Chicago Manual of Style (16th Edition):

Tonde, Chetan J., 1985-. “Supervised feature learning via dependency maximization.” 2016. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/50200/.

MLA Handbook (7th Edition):

Tonde, Chetan J., 1985-. “Supervised feature learning via dependency maximization.” 2016. Web. 31 Oct 2020.

Vancouver:

Tonde, Chetan J. 1. Supervised feature learning via dependency maximization. [Internet] [Doctoral dissertation]. Rutgers University; 2016. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/50200/.

Council of Science Editors:

Tonde, Chetan J. 1. Supervised feature learning via dependency maximization. [Doctoral Dissertation]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/50200/

3. Chen, He, 1990-. Molecular geometry optimization by artificial neural networks.

Degree: MS, Computer Science, 2019, Rutgers University

 Articial neural network is revolutionizing many areas in science and technology. We applied articial neural network to solve a non-linear optimization problem in computational chemistry,… (more)

Subjects/Keywords: Neural networks (Computer science); Stereochemistry

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APA (6th Edition):

Chen, He, 1. (2019). Molecular geometry optimization by artificial neural networks. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60070/

Chicago Manual of Style (16th Edition):

Chen, He, 1990-. “Molecular geometry optimization by artificial neural networks.” 2019. Masters Thesis, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/60070/.

MLA Handbook (7th Edition):

Chen, He, 1990-. “Molecular geometry optimization by artificial neural networks.” 2019. Web. 31 Oct 2020.

Vancouver:

Chen, He 1. Molecular geometry optimization by artificial neural networks. [Internet] [Masters thesis]. Rutgers University; 2019. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60070/.

Council of Science Editors:

Chen, He 1. Molecular geometry optimization by artificial neural networks. [Masters Thesis]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60070/

4. Shen, Jie, 1989-. Learning from structured data: theory, algorithms, and applications.

Degree: PhD, Computer Science, 2018, Rutgers University

 The last few years have witnessed the rise of the big data era, which features the prevalence of data sets that are high-dimensional, noisy, and… (more)

Subjects/Keywords: Big data; Algorithms

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shen, Jie, 1. (2018). Learning from structured data: theory, algorithms, and applications. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/59227/

Chicago Manual of Style (16th Edition):

Shen, Jie, 1989-. “Learning from structured data: theory, algorithms, and applications.” 2018. Doctoral Dissertation, Rutgers University. Accessed October 31, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/59227/.

MLA Handbook (7th Edition):

Shen, Jie, 1989-. “Learning from structured data: theory, algorithms, and applications.” 2018. Web. 31 Oct 2020.

Vancouver:

Shen, Jie 1. Learning from structured data: theory, algorithms, and applications. [Internet] [Doctoral dissertation]. Rutgers University; 2018. [cited 2020 Oct 31]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/59227/.

Council of Science Editors:

Shen, Jie 1. Learning from structured data: theory, algorithms, and applications. [Doctoral Dissertation]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/59227/

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