英语翻译A difficult problem with learning in many real-world dom

英语翻译
A difficult problem with learning in many real-world domains is that the concept of interest may depend on some hidden context,not given explicitly in the form of predictive features.A typical example is weather prediction rules that may vary radically with the season.Another example is the patterns of customers’ buying preferences that may change with time,depending on the current day of the week,availability of alternatives,inflation rate,etc.Often the cause of change is hidden,not known a priori,making the learning task more complicated.Changes in the hidden context can induce more or less radical changes in the target concept,which is generally known as concept drift (Widmer and Kubat,1996).An effective learner should be able to track such changes and to quickly adapt to them.
A difficult problem in handling concept drift is distinguishing between true concept drift and noise.Some algorithms may overreact to noise,erroneously interpreting it as concept drift,while others may be highly robust to noise,adjusting to the changes too slowly.An ideal learner should combine robustness to noise and sensitivity to concept drift (Widmer and Kubat,1996).
兄弟你速度好快，可惜了，我要的不是在线翻译

在现实世界的多个领域中存在的一个学习方面的困难是：“利率”的含义可能由一些隐含的信息而确定,不能以一些可预测的特征来表示出.一个典型例子就是随着季节变化而发生根本变动的天气预报,另一个例子则是可能随着时间而变化的消费者购买偏好模式,这种偏好的改变依赖于某一周内当期的那个时点、选择性、通货膨胀率等因素.而引起改变的原因常常是隐含的,不能提前得知,这就使得学习任务变得更复杂.发生于隐性环境中的改变,能或多或少地导致在目标环境中的根本改变,这被公认为“概念迁移”.一个高效学习者应该能够跟踪这些变化并且能迅速适应它们.
在处理“概念迁移”过程中存在一个困难,即区分真正的“概念漂移”和“噪音”.一些计算方法可能对噪音过度反应,把噪音错误地解释为概念迁移,而其他算法可能高度增强噪音,对于这些改变的调整过于缓慢.一个理想的学习者应该把对噪音的增强和对概念迁移的敏感有机结合起来.