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: Algorithmic Learning Theory; 6th International Wo Klaus P. Jantke,Takeshi Shinohara,Thomas Zeugmann Conference proceedings 1995 Springer-Ve

[復制鏈接]
樓主: 街道
41#
發(fā)表于 2025-3-28 15:53:25 | 只看該作者
42#
發(fā)表于 2025-3-28 19:31:40 | 只看該作者
43#
發(fā)表于 2025-3-29 02:11:09 | 只看該作者
44#
發(fā)表于 2025-3-29 05:30:16 | 只看該作者
Gründung und Errichtung der Kreditinstituteormulas is learnable with membership, equivalence and subset queries. Moreover, it is shown that under some condition the class of orthogonal .-Horn formulas is learnable with membership and equivalence queries.
45#
發(fā)表于 2025-3-29 10:34:49 | 只看該作者
46#
發(fā)表于 2025-3-29 14:35:27 | 只看該作者
?Bankbetrieb“ und ?Bankbetriebslehre“above, we obtain probabilistic hierarchies highly structured without a “gap” between the probabilistic and deterministic learning classes. In the case of exact probabilistic learning, we are able to show the probabilistic hierarchy to be dense for every mentioned monotonicity condition. Considering
47#
發(fā)表于 2025-3-29 18:50:55 | 只看該作者
Learning unions of tree patterns using queries,time PAC-learnability and the polynomial time predictability of .. when membership queries are available. We also show a lower bound . of the number of queries necessary to learn .. using both types of queries. Further, we show that neither types of queries can be eliminated to achieve efficient lea
48#
發(fā)表于 2025-3-29 23:31:36 | 只看該作者
49#
發(fā)表于 2025-3-30 00:29:20 | 只看該作者
Machine induction without revolutionary paradigm shifts,nference, it is shown that there are classes learnable . the non-revolutionary constraint (respectively, with severe parsimony), up to (i}+1) mind changes, and no anomalies, which classes cannot be learned with no size constraint, an unbounded, finite number of anomalies in the final program, but wi
50#
發(fā)表于 2025-3-30 06:32:32 | 只看該作者
Probabilistic language learning under monotonicity constraints,above, we obtain probabilistic hierarchies highly structured without a “gap” between the probabilistic and deterministic learning classes. In the case of exact probabilistic learning, we are able to show the probabilistic hierarchy to be dense for every mentioned monotonicity condition. Considering
 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, 2025-10-9 19:32
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
瑞金市| 牡丹江市| 永和县| 沭阳县| 中牟县| 修水县| 师宗县| 全椒县| 通山县| 柞水县| 皮山县| 广东省| 墨竹工卡县| 芦山县| 蒙自县| 丹棱县| 江油市| 海口市| 武穴市| 平邑县| 韶山市| 曲阜市| 信阳市| 瑞昌市| 邢台市| 云龙县| 永和县| 汶上县| 永仁县| 鄂托克旗| 桑日县| 普洱| 文登市| 县级市| 昔阳县| 福清市| 东方市| 改则县| 修文县| 凤庆县| 泸溪县|