Based on the idea of increment learning, the paper presents a new algorithm for the adaptive learning mechanism in the task of topic tracking.
该方法采用增量学习的思想，对话题追踪任务中的自适应学习机制提出了新的算法。
This method adopts the idea of increment learning, and presents new algorithm to the adaptive learning mechanism in the task of topic tracking.
该方法采用增量学习的思想，对话题追踪任务中的自适应学习机制提出了新的算法。
At first we modify the logical rules according to the feedback information to improve the ability of identify of the new illegitimate content and to implement the increment learning.
我们根据误判文档的反馈信息修改逻辑规则，使其不断增加对新非法文档的识别能力，实现规则的增量式学习。
To solve these problems, based on minimizing the increment of learning errors and combining LVQ and GNG, the authors propose a new growing LVQ method and apply it to text classification.
针对这些问题，基于最小化学习误差的增量思想，该文将学习型矢量量化（LVQ）和生长型神经气（GNG）结合起来提出一种新的增量学习型矢量量化方法，并将其应用到文本分类中。
To solve these problems, based on minimizing the increment of learning errors and combining LVQ and GNG, the authors propose a new growing LVQ method and apply it to text classification.
针对这些问题，基于最小化学习误差的增量思想，该文将学习型矢量量化（LVQ）和生长型神经气（GNG）结合起来提出一种新的增量学习型矢量量化方法，并将其应用到文本分类中。
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