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科技部學研示範上傳資料集Description in English:
[MT] Science and Technology Department Academic Demonstration Upload Information Collection -
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MADELON is an artificial dataset, which was part of the NIPS 2003 feature selection challenge. This is a two-class classification problem with continuous input variables. The...Description in English:
MADELON is an artificial dataset, which was part of the NIPS 2003 feature selection challenge. This is a two-class classification problem with continuous input variables. The difficulty is that the problem is multivariate and highly non-linear. -
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以virtuoso建立之鍊結開放資料示範服務，以產銷履歷相關為參考應用。Description in English:
[MT] The link to Virtuoso is open to open data demonstration services, based on the production and sales history. -
2 Views
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This dataset has no description
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GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits '4' and '9'. This dataset is one of five datasets of the NIPS 2003...Description in English:
GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits '4' and '9'. This dataset is one of five datasets of the NIPS 2003 feature selection challenge. -
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20 photos of leaves for each of 32 different species.Description in English:
20 photos of leaves for each of 32 different species. -
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DOROTHEA is a drug discovery dataset. Chemical compounds represented by structural molecular features must be classified as active (binding to thrombin) or inactive. This is one...Description in English:
DOROTHEA is a drug discovery dataset. Chemical compounds represented by structural molecular features must be classified as active (binding to thrombin) or inactive. This is one of 5 datasets of the NIPS 2003 feature selection challenge. -
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DEXTER is a text classification problem in a bag-of-word representation. This is a two-class classification problem with sparse continuous input variables. This dataset is one...Description in English:
DEXTER is a text classification problem in a bag-of-word representation. This is a two-class classification problem with sparse continuous input variables. This dataset is one of five datasets of the NIPS 2003 feature selection challenge. -
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Marine sponges of the Demospongiae class classification domain.Description in English:
Marine sponges of the Demospongiae class classification domain. -
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本平台相關手冊、說明文件Description in English:
[MT] This platform related manual, explanation file -
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A Free, Open Resource for the Global Research Community In response to the COVID-19 pandemic, the Allen Institute for AI has partnered with leading research groups to prepare...Description in English:
A Free, Open Resource for the Global Research CommunityIn response to the COVID-19 pandemic, the Allen Institute for AI has partnered with leading research groups to prepare and distribute the COVID-19 Open Research Dataset (CORD-19), a free resource of... -
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AutoUniv is an advanced data generator for classifications tasks. The aim is to reflect the nuances and heterogeneity of real data. Data can be generated in .csv, ARFF or C4.5...Description in English:
AutoUniv is an advanced data generator for classifications tasks. The aim is to reflect the nuances and heterogeneity of real data. Data can be generated in .csv, ARFF or C4.5 formats. -
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ARCENE's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This...Description in English:
ARCENE's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This dataset is one of 5 datasets of the NIPS 2003 feature selection challenge. -
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This dataset has no description
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為本學會線上申請資料之相關文件。Description in English:
[MT] For the relevant documents of the application materials for this school. -
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This dataset has no description
Funder:IDB, MOEA, R.O.C
Supervisor:TCA
Principal Investigator:Dr. Cheng-Kiang Farn
Co-Principal Investigator:Dr. Chi-Feng Tai
Co-Principal Investigator:Dr. Hsiao-Fen Chen
Programmer:Zhi-Yi Luo