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You searched for `+publisher:"Rutgers University" +contributor:("Awasthi, Pranjal")`

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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

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/49973/

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/50200/

►

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/60070/

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/59227/

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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/