对模型中每个上下文的预测概率从频率数计算，进行自适应更新。
Prediction probabilities for each context in the model are calculated from frequency counts, which are updated adaptively.
它使用有限的上下文统计建模技术，混合了多个顺序固定的上下文模型，预测输入序列中的下一个字符。
It USES a finite context statistical modeling technique that blends together several fixed-order context models to predict the next character in the input sequence.
该算法主要包括三个特色技术：基于纹线局部走向的分类预测、体现指纹微观纹理的扩展上下文以及基于成像仪器的分类熵编码器概率模型初始化。
There are mainly three distinguishing features in our proposed algorithm:local direction-based prediction, extended context for micro texture and histogram initialization based on imaging apparatus.
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