找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Computer Recognition Systems 4; Robert Burduk,Marek Kurzyński,Andrzej ?o?nierek Conference proceedings 2011 Springer Berlin Heidelberg 201

[復制鏈接]
樓主: risky-drinking
11#
發(fā)表于 2025-3-23 12:59:49 | 只看該作者
Evaluation of Reliability of a Decision-Making Process Based on Pattern Recognition, medicine, or biology, mechanisms assisting the decision-making process facilitate work significantly. One of the key issues is the reliability of made decisions. However, this issue is often passed over due to great increase in the amount of data processed by those systems, what forces the designers to focus on the systems’ efficiency.
12#
發(fā)表于 2025-3-23 17:21:31 | 只看該作者
13#
發(fā)表于 2025-3-23 20:22:00 | 只看該作者
14#
發(fā)表于 2025-3-24 01:27:35 | 只看該作者
Pose Invariant Face Recognition Method Using Stereo Visioninvisible due to occlusion, Eigenfaces recognition method is extended to operate on half-face images. Presented technique is verified experimentally to prove that it can be used to increase performance of image-based face recognition methods in applications where head pose of the subject is not controlled.
15#
發(fā)表于 2025-3-24 02:28:41 | 只看該作者
Subspace Algorithms for Face Verificationass (person). This work presents two such methods: one based on SDF and the other inspired by Clafic algorithm. In the experimental section they are compared to the two-class SVM on the realistic data set taken from CMU-PIE database. The results confirm the advantages of subspace approach.
16#
發(fā)表于 2025-3-24 06:49:28 | 只看該作者
17#
發(fā)表于 2025-3-24 13:48:21 | 只看該作者
18#
發(fā)表于 2025-3-24 16:19:16 | 只看該作者
Radial Basis Function Kernel Optimization for Pattern Classificationir application in kernel optimization is verified. Alternative evaluation measures that outperform presented methods are proposed.Optimization leveraging these measures results in parameters corresponding to the classifiers that achieve minimal error rate for . kernel.
19#
發(fā)表于 2025-3-24 22:42:20 | 只看該作者
Unified View of Decision Tree Learning Machines for the Purpose of Meta-learning the same environment. This is the start point for a manifold research in the area of DTs, which will bring advanced meta-learning algorithms providing new knowledge about DT induction and optimal DT models for many kinds of data.
20#
發(fā)表于 2025-3-25 02:13:52 | 只看該作者
 關于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結 SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-6 16:08
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
县级市| 互助| 克什克腾旗| 开化县| 乌拉特中旗| 邢台市| 沈丘县| 丰镇市| 黎城县| 阳高县| 永年县| 密云县| 靖边县| 镇平县| 扎赉特旗| 兖州市| 宜章县| 蕲春县| 定南县| 通河县| 双鸭山市| 新昌县| 时尚| 南涧| 工布江达县| 安国市| 绥棱县| 景谷| 卢湾区| 镇江市| 忻城县| 响水县| 剑川县| 东台市| 米易县| 元朗区| 文昌市| 竹北市| 利辛县| 新干县| 秦安县|