This is the Windows app named Active Learning whose latest release can be downloaded as active-learningsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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Ikut arahan ini untuk menjalankan apl ini:
- 1. Memuat turun aplikasi ini dalam PC anda.
- 2. Masukkan dalam pengurus fail kami https://www.onworks.net/myfiles.php?username=XXXXX dengan nama pengguna yang anda mahukan.
- 3. Muat naik aplikasi ini dalam pengurus filem tersebut.
- 4. Mulakan mana-mana emulator dalam talian OS OnWorks daripada tapak web ini, tetapi emulator dalam talian Windows yang lebih baik.
- 5. Daripada OS Windows OnWorks yang baru anda mulakan, pergi ke pengurus fail kami https://www.onworks.net/myfiles.php?username=XXXX dengan nama pengguna yang anda mahukan.
- 6. Muat turun aplikasi dan pasangnya.
- 7. Muat turun Wine dari repositori perisian pengedaran Linux anda. Setelah dipasang, anda kemudian boleh mengklik dua kali aplikasi untuk menjalankannya dengan Wine. Anda juga boleh mencuba PlayOnLinux, antara muka mewah melalui Wine yang akan membantu anda memasang program dan permainan Windows yang popular.
Wain ialah cara untuk menjalankan perisian Windows pada Linux, tetapi tanpa Windows diperlukan. Wain ialah lapisan keserasian Windows sumber terbuka yang boleh menjalankan program Windows secara langsung pada mana-mana desktop Linux. Pada asasnya, Wine cuba untuk melaksanakan semula Windows yang mencukupi dari awal supaya ia boleh menjalankan semua aplikasi Windows tersebut tanpa memerlukan Windows.
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DESCRIPTION
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks.
Ciri-ciri
- Modular experimentation framework for active learning research
- Supports multiple datasets and models including SVMs, logistic regression, and CNNs
- Implements a variety of active learning strategies such as margin sampling and k-center greedy
- Allows flexible configuration of parameters such as batch size, warm start ratio, and noise control
- Easy integration of new models and sampling methods through an extensible API
- Provides comprehensive benchmarking and analysis tools for experimental comparison
Bahasa Pengaturcaraan
Python
Kategori
This is an application that can also be fetched from https://sourceforge.net/projects/active-learning.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
