
Chinese: 
Boosting 是 Bagging 的一种增强版
通过改善系统表现不佳的地方 提高学习器的效果
这一领域最有名的算法之一是 Ada Boost
我认为应该读成 ada 而不是 ata 因为 ada 是 adaptive 的简写形式
下面说说 Ada Boost 的原理
正常地构建第一个数据袋
从训练数据中随机选择数据
正常地训练模型
下一步有所不同
我们用所有训练数据测试该模型
从而发现某些点（x 和 y）
预测得不够准确
某些点的误差
很大
现在构建下个数据袋
同样 从原始数据中随机选择数据

English: 
Boosting is a fairly simple variation
on bagging that strives to improve
the learners by focusing on areas where
the system is not performing well.
One of the most well-known algorithms
in this area is called ada boost.
And I believe it's ada,
not ata because ada stands for adaptive.
Here's how ada boost works.
We build our first bag of
data in the usual way.
We select randomly
from our training data.
We then train a model in a usual way.
The next thing we do, and
this is something different,
we take all our training data and
use it to test the model
in order to discover that
some of the points in here,
our x's and our y's,
are not well predicted.
So there's going to be
some points in here for
which there is significant error.
Now, when we go to build our
next bag of data, again,
we choose randomly
from our original data.

English: 
But each instance is weighted
according to this error.
So, these points that had significant
error, are more likely to get picked and
to go into this bag than any
other individual instance.
So as you see, we ended up with
a few of those points in here and
a smattering of all
the other ones as well.
We build a model from this data and
then we test it.
Now we test our system altogether.
In other words, we've got a sort
of miniature ensemble here,
just two learners.
And we test both of them.
We test them by inputting
again this in-sample data.
We test on each instance and
we combine their outputs.
And again we measure error
across all this data.
Maybe this time these points
got modeled better, but
there were some other ones up
here that weren't as good.
And thus we build our next bag and
our next model.
And we just continue this over,
and over and

Chinese: 
但是根据这个误差设定每个样本的权重
误差很大的这些点比其他任何单个样本
更有可能被选中 并进入这个袋里
可以看出 这里面有这几个点
还有其他点
我们用这些数据构建一个模型 然后测试它
现在测试整个系统
这是一个迷你集成学习器
只有两个学习器
同时测试这两个
使用这些样本数据测试模型
用到每个样本并将输出组合起来
用所有数据衡量误差
或许这次这些点建模效果更好
但是还有其他效果不好的点
所以构建下个小袋和下个模型
不断重复这一流程

English: 
over again up until m or
the total number of bags we'll be using.
So to recap, bagging,
when we build one of these instances,
is simply choosing some subset of
the data at random with replacement,
and we create each bag in the same way.
Boosting is an add-on to this idea where
in subsequent bags we choose those
data instances that had been modeled
poorly in the overall system before.

Chinese: 
直到一共有 m 个袋
总结下 对于 Bagging
我们只是抽出放回地随机选择数据子集
并以同样的方式创建每个袋
Boosting 是这种方法的增强版 在后续袋中
我们选择在之前的整体系统中建模效果差的样本
