Your Prediction Gets As Good As Your Data May 5, 2015 by Kazem In the past, we have seen software engineers and data scientists assume that they can keep increasing their prediction accuracy by improving their machine learning algorithm. Here, we wan…

Lessons Learned from Developing a Data Product For an assignment I was asked to develop a visual ‘data product’ that informed decisions on video game ratings taking as an indicator their ranking on the MetaCritic site. I decided to use RStudio’s Shin…

There are a number of algorithms that are typically used for system identification, adaptive control, adaptive signal processing, and machine learning. These algorithms all have particular similarities and differences. However, they all need to proce…

微软近期Open的职位: Job Description Group: Search Technology Center Asia (STCA)/Search Ads Title: SDEII-Senior SDE Location: Beijing, China STCA was founded in year 2005 and is now starting the second "Five-Year Plan" after the merge with the Ads Platf…

手写数字digits分类,这可是深度学习算法的入门练习.而且还有专门的手写数字MINIST库.opencv提供了一张手写数字图片给我们,先来看看 这是一张密密麻麻的手写数字图:图片大小为1000*2000,有0-9的10个数字,每5行为一个数字,总共50行,共有5000个手写数字.在opencv3.0版本中,图片存放位置为 /opencv/sources/samples/data/digits.png 我们首先要做的,就是把这5000个手写数字,一个个截取出来,每个数字块大小为20*20.直接将…

OCR (Optical Character Recognition,光学字符识别),我们这个练习就是对OCR英文字母进行识别.得到一张OCR图片后,提取出字符相关的ROI图像,并且大小归一化,整个图像的像素值序列可以直接作为特征.但直接将整个图像作为特征数据维度太高,计算量太大,所以也可以进行一些降维处理,减少输入的数据量. 处理过程一般这样:先对原图像进行裁剪,得到字符的ROI图像,二值化.然后将图像分块,统计每个小块中非0像素的个数,这样就形成了一个较小的矩阵,这矩阵就是新的特征了.ope…

1.脚本 This directory includes some useful codes: 1. subset selection tools. (子集抽取工具) subset.py 2. parameter selection tools. (参数选优工具) grid.py 3. LIBSVM format checking tools(格式检查工具)checkdata.py Part I: Subset selection tools子集抽取 Introduction =========…

图解Nagios的工作原理 Nagios的主动模式和被动模式 被动模式:就如同上图所显示的那样,客户端起nrpe进程,服务端通过check_nrpe插件向客户端发送命令,客户端根据服务端的指示来调用相应的插件,插件可以获取到本机的相关信息,并把获取到的结果发送给服务端.因为需要调用客户端的插件去等带客户端返回的信息,所以叫做被动模式 主动模式:主动模式不需要调用客户端的插件,而是通过自己的插件主动去探测客户端的相关信息. 那么,因为主动模式和被动模式的区别这两种模式所擅长监控的服务也是不同的.…

序列标注(sequence labelling),输入序列每一帧预测一个类别.OCR(Optical Character Recognition 光学字符识别). MIT口语系统研究组Rob Kassel收集,斯坦福大学人工智能实验室Ben Taskar预处理OCR数据集(http://ai.stanford.edu/~btaskar/ocr/ ),包含大量单独手写小写字母,每个样本对应16X8像素二值图像.字线组合序列,序列对应单词.6800个,长度不超过14字母的单词.gzip压缩,内容用T…

另外一篇文章地址:这个比较详细,但是程序略显简单,现在这个程序是比较复杂的 http://blog.csdn.net/wangyaninglm/article/details/17091901 整个项目下载地址: http://download.csdn.net/detail/wangyaninglm/8244549 实现效果: Finger.h #ifndef __TOUCHSCREEN_FINGER__ #define __TOUCHSCREEN_FINGER__ #include <cxc…

转摘自http://www.cnblogs.com/denny402/p/5032839.html opencv3中的ml类与opencv2中发生了变化,下面列举opencv3的机器学习类方法实例: 用途是opencv自带的ocr样本的分类功能,其中神经网络和adaboost训练速度很慢,效果还是knn的最好: #include <opencv2/opencv.hpp> #include <iostream> using namespace std; using namespace…

Datasets and Estimators are two key TensorFlow features you should use: Datasets: The best practice way of creating input pipelines (that is, reading data into your program). Estimators: A high-level way to create TensorFlow models. Estimators includ…

AIOps platforms enhance IT operations through greater insights by combining big data, machine learning and visualization. I&O leaders should initiate AIOps deployment to refine performance analysis today and augment to IT service management and autom…

# file: dt_cls_dense_batch.py #=============================================================================== # Copyright 2014-2018 Intel Corporation. # # This software and the related documents are Intel copyrighted materials, and # your use of the…

https://developer.valvesoftware.com/wiki/Latency_Compensating_Methods_in_Client/Server_In-game_Protocol_Design_and_Optimization Overview Designing first-person action games for Internet play is a challenging process. Having robust on-line gameplay in…

Understanding the Bias-Variance Tradeoff When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to "bias" and error due to "variance". There is a tradeoff between a m…

==>转自:http://blog.163.com/crazyzcs@126/blog/static/129742050201061192243911/ http://www.ilovematlab.cn/viewthread.php?tid=74019&sid=vYpSs5 SVM相关资源汇总[matlab-libsvm-class-regress](by faruto)SVM相关资源汇总[matlab-libsvm-class-regress](by faruto)          …

2015年~2017年SIGIR,SIGKDD,ICML三大会议的Recsys论文: [转载请注明出处:https://www.cnblogs.com/shenxiaolin/p/8321722.html] SIGIR-2015 [Title]WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation [Abstract]Matrix approximation…

前一段时间做了一些项目,把一些笔记放在了txt中,现分享出来,自己也能够时长预习. 1) 读取文件时,将固定的文件地址,采用数组或者字符串的形式,提前表示出来,后期使用时候采用拼接操作 2) # 得到该目录下的文件 file_list = os.listdir(base_path + '/data/cnn_train/') file_list Out[6]: ['finance', 'it', 'sports'] 3) 打开一个文件 f = open(base_path + '/data/cnn…

In the previous post, we talk about a very popular Boosting algorithm - Gradient Boosting Decision Tree. The key of GBM is using Gradient Descent to optimize the loss function. But why Gradient Descent, not other numeric optimization method? Is it th…

ICLR 2013 International Conference on Learning Representations May 02 - 04, 2013, Scottsdale, Arizona, USA ICLR 2013 Workshop Track Accepted for Oral Presentation Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Hamsa Sr…

车牌识别的属于常见的 模式识别 ,其基本流程为下面三个步骤: 1) 分割: 检测并检测图像中感兴趣区域: 2)特征提取: 对字符图像集中的每个部分进行提取: 3)分类: 判断图像快是不是车牌或者 每个车牌字符的分类. 车牌识别分为两个步骤, 车牌检测, 车牌识别, 都属于模式识别. 基本结构如下: 一.车牌检测 1.车牌局部化(分割车牌区域),根据尺寸等基本信息去除非车牌图像: 2.判断车牌是否存在 (训练支持向量机 -svm, 判断车牌是否存在). 二.车牌识别 1.字符局部化(分割字符),根…

1. Overview 本文将CNN用于句子分类任务 (1) 使用静态vector + CNN即可取得很好的效果:=> 这表明预训练的vector是universal的特征提取器,可以被用于多种分类任务中. (2) 根据特定任务进行fine-tuning 的vector + CNN 取得了更好的效果. (3) 改进模型架构,使得可以使用 task-specific 和 static 的vector. (4) 在7项任务中的4项取得了SOTA的效果. 思考:卷积神经网络的核心思想是捕获局部特征.在…

以代码的思想去详细讲解yolov3算法的实现原理和训练过程,并教使用visdrone2019数据集和自己制作数据集两种方式去训练自己的pytorch搭建的yolov3模型,吐血整理万字长文,纯属干货 ! 实现思路 第一步:Pytorch搭建yolo3目标检测平台 模型yolov3和预训练权重下载 yolo3算法原理实现思路 一.预测部分 1.yolo3的网络模型架构和实现 2.主干特征网络darknet53介绍和结果(获取3个初始特征层) 3.从初始特征获取预测结果(最终的3个有效的特征层) 4…

经Edwin Chen的推荐,认识了scikit-learn这个非常强大的python机器学习工具包.这个帖子作为笔记.(其实都没有笔记的意义,因为他家文档做的太好了,不过还是为自己记记吧,为以后节省若干分钟).如果有幸此文被想用scikit-learn的你看见,也还是非常希望你去它们的主页看文档.主页中最值得关注的几个部分:User Guide几乎是machine learning的索引,各种方法如何使用都有,Reference是各个类的用法索引. S1. 导入数据 大多数数据的格式都是M个N…

原文地址:https://cn.mathworks.com/help/fuzzy/examples/chaotic-time-series-prediction.html?requestedDomain=www.mathworks.com This example shows how to do chaotic time series prediction using ANFIS. Time Series Data The data is generated from the Mackey-Gl…

LSTM NEURAL NETWORK FOR TIME SERIES PREDICTION Wed 21st Dec 2016   Neural Networks these days are the "go to" thing when talking about new fads in machine learning. As such, there's a plethora of courses and tutorials out there on the basic vani…

机器学习中离散特征的处理方法 Updated: August 25, 2016 Learning with counts is an efficient way to create a compact set of features for a dataset, based on counts of the values. You can use the modules in this section to build a set of counts and features, and late…

Abstract – In many practical data mining applications such as web page classification, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as co-traini…

ABSTRACT Recent technological advancement have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The…