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en:projects:details:neuralsemproject2 [2013/02/05 14:15] amir |
en:projects:details:neuralsemproject2 [2016/06/23 11:26] (current) |
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| The database entry: | The database entry: | ||
| "type" is one of the following: phd theses, phd semester, master thesis, master semester, bachelor semester | "type" is one of the following: phd theses, phd semester, master thesis, master semester, bachelor semester | ||
| - | "status" is one of the following: available, taken, completed (please upgrade accordingly!!!!!!!!!!) | + | "state" is one of the following: available, taken, completed (please upgrade accordingly!!!!!!!!!!) |
| "by" should be filled as soon as the project is taken/completed | "by" should be filled as soon as the project is taken/completed | ||
| "completed_dt" is the date when the project was completed (YYYY-MM-DD). | "completed_dt" is the date when the project was completed (YYYY-MM-DD). | ||
| Line 17: | Line 17: | ||
| contactroom: BC 160 | contactroom: BC 160 | ||
| type : master semester | type : master semester | ||
| - | status : taken | + | state : completed |
| created_dt : 2011-09-23 | created_dt : 2011-09-23 | ||
| taken_dt : 2013-01-17 | taken_dt : 2013-01-17 | ||
| - | completed_dt : YYYY-MM-DD | + | completed_dt : 2013-06-12 |
| by : Michael Hobbs | by : Michael Hobbs | ||
| - | output_media : en:projects:neural_storage_capacity.pdf|Download Abstract in PDF Format | + | output_media : :en:projects:master_semester:hobbs_salavati_semester_project_report_2013.pdf|Download Project Report in PDF Format |
| table : projects | table : projects | ||
| ====== | ====== | ||
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| \\ | \\ | ||
| /* Description of the project */ | /* Description of the project */ | ||
| - | ===== Background ===== | + | ===== Project Description ===== |
| \\ | \\ | ||
| Dimensionality reduction is a widely used technique in machine learning and data processing. Since dealing with large amount of high dimensional data is difficult, one could use dimension reduction to reduce the number of variables (dimensions) and then use this coarse version of input data for further processing. | Dimensionality reduction is a widely used technique in machine learning and data processing. Since dealing with large amount of high dimensional data is difficult, one could use dimension reduction to reduce the number of variables (dimensions) and then use this coarse version of input data for further processing. | ||
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| To read more about nonlinear dimensionality reduction, here are the corresponding wikipedia entries [[http://en.wikipedia.org/wiki/Dimension_reduction|Dimension Reduction]] and [[http://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction|Nonlinear Dimensionality Reduction]]. | To read more about nonlinear dimensionality reduction, here are the corresponding wikipedia entries [[http://en.wikipedia.org/wiki/Dimension_reduction|Dimension Reduction]] and [[http://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction|Nonlinear Dimensionality Reduction]]. | ||
| + | \\ | ||
| + | \\ | ||
| + | \\ | ||
| + | ====== Report ====== | ||
| + | The report is available via the following link: | ||
| + | \\ | ||
| + | {{:en:projects:master_semester:hobbs_salavati_semester_project_report_2013.pdf|Nonlinear Dimensionality Reduction Techniques and Their Application in Neural Networks}} | ||