alyato

Training phase

20000 training instances The number of support vectors is around 15000, which means that most of the training data are near the separating hyper-plane. The training accuracy is around 80%

Test on full data

Accuracy = 52.3734% (662/1264) (classification)
Accuracy = 62.5791% (791/1264) (classification)
Accuracy = 61.0759% (772/1264) (classification)
Accuracy = 60.9968% (771/1264) (classification)

学会基本操作后,值得细究的Mode:

CC Mode, 写C/C++代码, 管理缩进,高亮
Cperl Mode, 写Perl代码, 比Perl Mode更强.
Python Mode
eshell, M-x shell RET
Diff Mode / Ediff Mode, 看diff, 做merge操作.
VC, 与版本管理系统相接合,支持CVS, SVN, Git etc.
Hippie Expand, M-/
Dired Mode, 文件, 目录管理. copy, mv, rename. create

Clone the recursive repo for xgboost

git clone --recursive https://github.com/dmlc/xgboost
cd xgboost
make -j4

cd in the folder: xgboost/python-package

	import numpy as np

	from keras.datasets import imdb
	from keras.preprocessing.sequence import pad_sequences
	from keras.models import Sequential
	from keras.layers import containers
	from keras.layers.noise import GaussianNoise
	from keras.layers.core import Dense, AutoEncoder
	from keras.utils import np_utils
	from sklearn.metrics import (precision_score, recall_score,

	旧石器时代

	中石器时期

	黄河文明

	长江文明

	新石器时期