摘要
一位新闻记者兼机器人与机器学习每日新闻编辑在一份新的报告中对波尔兹曼机器的最新数据表示不满。根据NewsRx编辑对Universitas Muh Ammadiyah Malang的新闻报道，研究表明，"选择专业对一些学生来说并不容易，特别是对那些对自己的技能和能力缺乏信心的学生。"我们的新闻记者从穆罕默德·迪亚·马朗大学的研究中获得了一句话：“高等教育的专业化成为了学生未来职业成功的基准和关键。这项研究的目的是提供学生学习成果的记录。”利用2013-2015年信息专业毕业生的数据，对319名学生进行了专业分类，本研究采用的分类方法是受限的Boltzmann机器(RBM)，但由于每个领域的数量差异很大，数据显示出班级分布不平衡，因此增加了SMOTE对不平衡班级进行分类。根据新闻记者的说法，研究得出结论：“RBM和SMOTE组合获得的准确率为70%，平均平方误差为0.4.”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Boltzmann machines are p resented in a new report. According to news reporting out of the Universitas Muh ammadiyah Malang by NewsRx editors, research stated, “Choosing a specialization was not an easy task for some students, especially for those who lacked confiden ce in their skill and ability.” Our news journalists obtained a quote from the research from Universitas Muhamma diyah Malang: “Specialization in tertiary education became the benchmark and key to success for students’ future careers. This study was conducted to provide th e learning outcomes record, which showed the specialization classification for t he Informatics students by using the data from the students of 2013-2015 who had graduated. The total data was 319 students. The classification method used for this study was the Restricted Boltzmann Machine (RBM). However, the data showed imbalanced class distribution because the number of each field differed greatly. Therefore, SMOTE was added to classify the imbalanced class.” According to the news reporters, the research concluded: “The accuracy obtained from the combination of RBM and SMOTE was 70% with a 0.4 mean squa red error.”
NETL
NSTL