QA Official

K Nearest Neighbor-Iris Classification

https://qaofficial.com/post/2019/04/16/72861-k-nearest-neighbor-iris-classification.html 2019-04-16
The following uses KNN algorithm to classify biological species, and uses the most famous Iris data set.This data has been used by Fisher in classical papers, and is currently pre-stored in S Kearne's toolkit as textbook data samples. Python source code: #coding=utf-8 from sklearn.datasets import load_iris #------------- from sklearn.cross_validation import train_test_split #------------- from sklearn.preprocessing import StandardScaler from sklearn.neighbors import KNeighborsClassifier #------------- from sklearn.metrics import classification_report #-------------load data iris=load_iris() print 'data shape',iris.

R Language-Data Preprocessing (2)

https://qaofficial.com/post/2019/04/16/72883-r-language-data-preprocessing-2.html 2019-04-16
1, r data set related operationsThe output tells us that 104 samples are complete, 34 samples miss only the Ozone measurement, 4 samples miss only the Solar.R value and so on. tempData <- mice(data,m=5,maxit=50,meth='pmm',seed=500) summary(tempData) 其中m=5 代表输入数据集的个数,meth='pmm' 代表填充的方法,在

alv Multi-layer Display and Classification Summary

https://qaofficial.com/post/2019/04/16/72833-alv-multi-layer-display-and-classification-summary.html 2019-04-16
*&---------------------------------------------------------------------* *& Report ZFI_JXC_PRINT *& *&---------------------------------------------------------------------* REPORT ZFI_JXC_PRINT. tables: sscrfields,bseg,bkpf,mara. type-pools: slis,kkblo. data: w_vari type disvariant, w_layo type slis_layout_alv, i_fcat type slis_t_fieldcat_alv, w_fcat type slis_fieldcat_alv, i_evts type slis_t_event, i_evts_exit type slis_t_event_exit, w_keyinfo type slis_keyinfo_alv. constants: cns_x type c value 'X'. field-symbols:type table. field-symbols: type table. data:str_name1(30) type c, str_name2(30) type c. data: l_total type i value 0. data: x_total type i value 0. data: g_belnr like range of bkpf-belnr, g_belnr_line

Analysis of Common Database Scenarios

https://qaofficial.com/post/2019/04/16/72831-analysis-of-common-database-scenarios.html 2019-04-16
Common Database Scenarios Analysis 2 a relational database 2 1. Introduction to Relational Data 2 2. Common Concepts 2 4. Feature 3 5. Common Relational Databases 3 2 Common Hibari (NoSQL) Scenario Analysis 3 1, NoSQL feature 3 2, according to the needs of classification 4 2.1 Kye-Value Database Meeting Extremely High Read and Write Performance Requirements: Redis, Tokyo Cabinet, Flare 4

Building Convolutional Neural Network for Simple Picture Classification (3)-Testing and Application of Model

https://qaofficial.com/post/2019/04/16/72940-building-convolutional-neural-network-for-simple-picture-classification-3-testing-and-application-of-model.html 2019-04-16
Both functions are in the same file 1. create a new Disimage.py file import tensorflow as tf from PIL import Image import os import numpy as np import matplotlib.pyplot as plt from GetCnnData import get_files import CNN classes = [] n_classes = 0 #获取一张图片 def get_one_image(train): n = len(train) ind = np.random.randint(0, n) img_dir = train[ind] # 随机选择测试的

Change the Development Trend of IT Industry in the Future (Transfer)

https://qaofficial.com/post/2019/04/16/72948-change-the-development-trend-of-it-industry-in-the-future-transfer.html 2019-04-16
[color=green]原帖地址: [url]http://chn.blogbeta.com/251.html[/url][/color][

Classical Paper Sorting in Image Processing and Computer Vision

https://qaofficial.com/post/2019/04/16/72879-classical-paper-sorting-in-image-processing-and-computer-vision.html 2019-04-16
Before 1990 Peter Burt, Edward Adelson The Laplacian Pyramid as ACompact Image Code 虽说这个Laplacian Pyramid是有冗余的,但使用起来非常简单方便,对理解小波变换也非常有帮助。这位Ade

Definition of Target Detection

https://qaofficial.com/post/2019/04/16/72918-definition-of-target-detection.html 2019-04-16
Target detection and identification refers to finding targets from a scene (picture), including two processes of detection (where) and identification (what).The difficulty of the task lies in the extraction and identification of candidate regions to be detected. Therefore, the broad framework of the task is as follows: Firstly, a model for extracting candidate regions from the scene is established and then identify the classification model of the candidate region Finally fine-tune the parameters of the classification model and the location of valid candidate boxes Target detection and recognition has a wide range of applications in many fields of life.

Dynamic Video Target Detection and Tracking Technology (Getting Started)

https://qaofficial.com/post/2019/04/16/72891-dynamic-video-target-detection-and-tracking-technology-getting-started.html 2019-04-16
Dynamic Video Target Detection and Tracking Technology http://m.qingqingsk.com/ztnews/lvvozlzrztkzrqwqqlnrluqk.html The traditional TV monitoring technology can only achieve the function of "clairvoyance", transmitting remote target images (original data) to the monitoring center, and the monitoring personnel can make judgment on the scene according to the visual video images.The purpose of intelligent video surveillance is to convert the original video data into a sufficient amount of "useful information" for the monitoring personnel to make decisions, so that the monitoring personnel can understand the events in a timely and comprehensive manner: "

Image Classification and Detection for Deep Learning: Papers Cited Most in 2010-2016

https://qaofficial.com/post/2019/04/16/72869-image-classification-and-detection-for-deep-learning-papers-cited-most-in-2010-2016.html 2019-04-16
Recently, I saw an article that counted the most frequently cited in-depth study papers from 2010 to 2016. During the postgraduate period, the direction was object detection, so I intercepted some papers related to the field.Personally, I think that object detection can be divided into many detailed studies. It is a good job to find the pain points and propose the available solutions for any detail, including: the understanding of convolution neural network model (theory/network structure);Behavioral analysis of structural parameters (optimization/regularization);Performance in different data distributions.