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: Intelligent Data Engineering and Automated Learning -- IDEAL 2014; 15th International C Emilio Corchado,José A. Lozano,Hujun Yin Conference

[復(fù)制鏈接]
樓主: Malevolent
11#
發(fā)表于 2025-3-23 13:03:39 | 只看該作者
Diversified Random Forests Using Random Subspaces,, giving a weight to each subspace according to its predictive power, and using this weight in majority voting. Experimental study on 15 real datasets showed favourable results, demonstrating the potential of the proposed method.
12#
發(fā)表于 2025-3-23 17:11:40 | 只看該作者
Multi-step Forecast Based on Modified Neural Gas Mixture Autoregressive Model,g patterns and its suitability for various step-ahead predictions. Experimental results on several financial time series and benchmark data demonstrate the effectiveness of proposed method and markedly improvement performances over many existing neural networks.
13#
發(fā)表于 2025-3-23 21:50:18 | 只看該作者
LBP and Machine Learning for Diabetic Retinopathy Detection, patterns (LBP) to extract local features, while in the second stage, we have applied artificial neural networks, random forest and support vector machines for the detection task. Preliminary results show that random forest was the best classifier with 97.46% of accuracy, using a data set of 71 images.
14#
發(fā)表于 2025-3-24 02:11:33 | 只看該作者
15#
發(fā)表于 2025-3-24 03:42:44 | 只看該作者
16#
發(fā)表于 2025-3-24 08:49:02 | 只看該作者
17#
發(fā)表于 2025-3-24 11:23:08 | 只看該作者
Object-Neighbourhood Clustering Ensemble Method,tasets. The results show that our ensemble method outperforms the co-association method, when the Average linkage is used. Furthermore, the results show that our ensemble method is more accurate than the baseline algorithm, and this indicates that the clustering ensemble method is more consistent and reliable than a single clustering algorithm.
18#
發(fā)表于 2025-3-24 16:12:07 | 只看該作者
19#
發(fā)表于 2025-3-24 19:22:32 | 只看該作者
20#
發(fā)表于 2025-3-24 23:48:24 | 只看該作者
0302-9743 optimization, regression, classification, clustering, biological data processing, text processing, and image/video analysis.978-3-319-10839-1978-3-319-10840-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
 關(guān)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(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2026-2-6 00:51
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
和政县| 金门县| 神农架林区| 关岭| 乾安县| 峨边| 海口市| 新昌县| 驻马店市| 名山县| 广宗县| 峨山| 阿图什市| 景宁| 莱芜市| 淮安市| 台北市| 丹江口市| 玛多县| 佛冈县| 安图县| 云龙县| 修水县| 石楼县| 遂溪县| 昌黎县| 蕉岭县| 库伦旗| 嘉鱼县| 彰化市| 仙居县| 高陵县| 泰来县| 辉县市| 唐海县| 山阳县| 丹阳市| 石河子市| 和政县| 嘉义县| 哈尔滨市|