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: ;

[復(fù)制鏈接]
樓主: 減輕
31#
發(fā)表于 2025-3-26 22:28:26 | 只看該作者
A Multi-graph Spectral Framework for Mining Multi-source Anomalies,used in a variety of domains, such as intrusion detection, fraud detection, and health monitoring. Today’s information explosion generates significant challenges for anomaly detection when there exist many large, distributed data repositories consisting of a variety of data sources and formats.
32#
發(fā)表于 2025-3-27 01:46:14 | 只看該作者
Graph Embedding for Speaker Recognition,compassing multiple applications. At the core is the problem of speaker comparison—given two speech recordings (utterances), produce a score which measures speaker similarity. Using speaker comparison, other applications can be implemented—speaker clustering (grouping similar speakers in a corpus),
33#
發(fā)表于 2025-3-27 07:05:54 | 只看該作者
34#
發(fā)表于 2025-3-27 11:14:52 | 只看該作者
35#
發(fā)表于 2025-3-27 17:17:59 | 只看該作者
36#
發(fā)表于 2025-3-27 19:22:13 | 只看該作者
Iris Bednarz-Braun,Ulrike He?-Meiningused in a variety of domains, such as intrusion detection, fraud detection, and health monitoring. Today’s information explosion generates significant challenges for anomaly detection when there exist many large, distributed data repositories consisting of a variety of data sources and formats.
37#
發(fā)表于 2025-3-27 23:48:03 | 只看該作者
Improving Classifications Through Graph Embeddings,ng [5], medical diagnosis [15], demographic research [13], etc. Unsupervised classification using K-Means generally clusters data based on (1) distance-based attributes of the dataset [4, 16, 17, 23] or (2) combinatorial properties of a weighted graph representation of the dataset [8].
38#
發(fā)表于 2025-3-28 05:22:07 | 只看該作者
Learning with ,,-Graphfor High Dimensional Data Analysis,ce learning, and semi-supervised learning. Data clustering often starts with a pairwise similarity graph and then translates into a graph partition problem [19], and thus the quality of the graph essentially determines the clustering quality.
39#
發(fā)表于 2025-3-28 09:20:01 | 只看該作者
40#
發(fā)表于 2025-3-28 10:34:00 | 只看該作者
A Multi-graph Spectral Framework for Mining Multi-source Anomalies,used in a variety of domains, such as intrusion detection, fraud detection, and health monitoring. Today’s information explosion generates significant challenges for anomaly detection when there exist many large, distributed data repositories consisting of a variety of data sources and formats.
 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 期刊點評 投稿經(jīng)驗總結(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, 2025-10-8 12:33
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
廊坊市| 三门县| 青龙| 锡林郭勒盟| 龙岩市| 棋牌| 仁怀市| 奉节县| 新营市| 东阿县| 阳山县| 乌兰浩特市| 鹤峰县| 昭通市| 景东| 葫芦岛市| 塔河县| 大余县| 余干县| 长顺县| 花莲市| 罗平县| 平凉市| 古蔺县| 咸宁市| 乐平市| 青浦区| 寻甸| 民勤县| 湘潭县| 汪清县| 镇原县| 永兴县| 双辽市| 桐梓县| 阿拉善盟| 凤山县| 山东省| 永年县| 林甸县| 绥中县|