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Machine Learning | Safe Semi-Supervised Learning

Semi-supervised learning (SSL) concerns the problem of how to improve learning performance via the usage of a small amount of labeled data and a large amount of unlabeled data. Many SSL methods have been developed, e.g., generative model, graph-based method, disagreement-based method, and semi-supervised SVMs. Despite the success of SSL, however, a considerable amount of empirical studies reveal that SSL with the exploitation of unlabeled data might even deteriorate learning performance. It is highly desirable to study a safe SSL scheme that on one side could often improve performance, on the other side will not hurt performance, since the users of SSL won't expect that SSL with the usage of more data performs worse than certain direct supervised learning with only labeled data. #SafeSSL #DataScience #MachineLearning ====================================================================== If you would like to support this channel and my work through BHIM-UPI: 919870794639@federal 🐍𝑷𝒚𝒕𝒉𝒐𝒏 𝑺𝒌𝒊𝒍𝒍 𝑺𝒆𝒓𝒊𝒆𝒔 👉    • Skill Series - Python   📝 𝑵𝒂𝒕𝒖𝒓𝒂𝒍 𝑳𝒂𝒏𝒈𝒖𝒂𝒈𝒆 𝑷𝒓𝒐𝒄𝒆𝒔𝒔𝒊𝒏𝒈 👉    • Natural Language Processing   🐙𝑫𝒆𝒆𝒑 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 👉    • Deep Learning   🦾𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 👉   • Machine Learning   🤖𝑨𝒓𝒕𝒊𝒇𝒊𝒄𝒊𝒂𝒍 𝑰𝒏𝒕𝒆𝒍𝒍𝒊𝒈𝒆𝒏𝒄𝒆 👉   • Artificial Intelligence Tutorials   ☁️𝑪𝒍𝒐𝒖𝒅 𝑪𝒐𝒎𝒑𝒖𝒕𝒊𝒏𝒈 👉   • Cloud Computing Tutorials   📶𝑾𝒊𝒓𝒆𝒍𝒆𝒔𝒔 𝑻𝒆𝒄𝒉𝒏𝒐𝒍𝒐𝒈𝒚 👉   • Wireless Technology Tutorials   🛠️𝑫𝒂𝒕𝒂 𝑴𝒊𝒏𝒊𝒏𝒈 👉   • Data Mining & Business Intelligence   ⚙️𝑺𝒊𝒎𝒖𝒍𝒂𝒕𝒊𝒐𝒏 𝑴𝒐𝒅𝒆𝒍𝒊𝒏𝒈 👉   • Simulation Modeling Tutorials   🐘𝑩𝒊𝒈 𝑫𝒂𝒕𝒂 👉   • Big Data Anaytics   ⛓️𝑩𝒍𝒐𝒄𝒌𝒄𝒉𝒂𝒊𝒏 𝑻𝒆𝒄𝒉𝒏𝒐𝒍𝒐𝒈𝒚 👉   • Blockchain Technology   💡𝑰𝑶𝑻 👉   • Internet Of Things   𝓕𝓸𝓵𝓵𝓸𝔀 𝓶𝓮 𝓸𝓷 𝓘𝓷𝓼𝓽𝓪𝓰𝓻𝓪𝓶 👉   / adhyapakh   𝓥𝓲𝓼𝓲𝓽 𝓶𝔂 𝓟𝓻𝓸𝓯𝓲𝓵𝓮 👉   / reng99   𝓢𝓾𝓹𝓹𝓸𝓻𝓽 𝓶𝔂 𝔀𝓸𝓻𝓴 𝓸𝓷 𝓟𝓪𝓽𝓻𝓮𝓸𝓷 👉   / ranjiraj   𝓖𝓲𝓽𝓗𝓾𝓫👉 https://github.com/ranjiGT