標(biāo)題: Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Walter Daelemans,Bart Goethals,Katharina Morik Conference proce [打印本頁(yè)] 作者: risky-drinking 時(shí)間: 2025-3-21 19:08
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學(xué)科排名
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases被引頻次
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學(xué)科排名
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases年度引用
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases年度引用學(xué)科排名
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學(xué)科排名
作者: exercise 時(shí)間: 2025-3-21 20:44 作者: aesthetic 時(shí)間: 2025-3-22 02:41
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620517.jpg作者: compose 時(shí)間: 2025-3-22 08:03 作者: 漫步 時(shí)間: 2025-3-22 10:09
Exceptional Model Miningsomehow exceptional. We discuss regression as well as classification models, and define quality measures that determine how exceptional a given model on a subgroup is. Our framework is general enough to be applied to many types of models, even from other paradigms such as association analysis and graphical modeling.作者: 頑固 時(shí)間: 2025-3-22 16:46
A Joint Topic and Perspective Model for Ideological Discourse To cope with the non-conjugacy of the logistic-normal prior we derive a variational inference algorithm for the model. We evaluate the proposed model on synthetic data as well as a written and a spoken political discourse. Experimental results strongly support that ideological perspectives are reflected in lexical variations.作者: CORD 時(shí)間: 2025-3-22 19:55 作者: instulate 時(shí)間: 2025-3-22 21:36
Fitted Natural Actor-Critic: A New Algorithm for Continuous State-Action MDPs-spaces; in turn, the use of a regression-based critic allows for efficient use of data and avoids convergence problems that TD-based critics often exhibit. We establish the convergence of our algorithm and illustrate its application in a simple continuous space, continuous action problem.作者: 使成核 時(shí)間: 2025-3-23 05:04 作者: orthopedist 時(shí)間: 2025-3-23 08:58 作者: linear 時(shí)間: 2025-3-23 09:42 作者: scoliosis 時(shí)間: 2025-3-23 16:56 作者: 啟發(fā) 時(shí)間: 2025-3-23 20:14 作者: 法律 時(shí)間: 2025-3-23 23:21 作者: Pander 時(shí)間: 2025-3-24 04:31 作者: Finasteride 時(shí)間: 2025-3-24 08:32
Conference proceedings 2008in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Jo作者: linear 時(shí)間: 2025-3-24 10:45 作者: dendrites 時(shí)間: 2025-3-24 15:07 作者: carotenoids 時(shí)間: 2025-3-24 22:36
Kernel-Based Inductive Transfer the new data. The criterion is based on a meta kernel capturing the similarity of two datasets. In experiments on small molecule and text data, kernel-based inductive transfer showed a statistically significant improvement over the best individual kernel in almost all cases.作者: 洞察力 時(shí)間: 2025-3-25 02:14
Improving Classification with Pairwise Constraints: A Margin-Based Approachse constraints into the conventional margin-based learning framework. We also present an efficient algorithm, PCSVM, to solve the pairwise constraint learning problem. Experiments with 15 data sets show that pairwise constraint information significantly increases the performance of classification.作者: 飛行員 時(shí)間: 2025-3-25 05:50 作者: 分開(kāi)如此和諧 時(shí)間: 2025-3-25 10:32 作者: 糾纏 時(shí)間: 2025-3-25 12:34 作者: 柔軟 時(shí)間: 2025-3-25 18:53
Tetsuro Morimura,Eiji Uchibe,Junichiro Yoshimoto,Kenji Doya作者: concert 時(shí)間: 2025-3-25 23:36
Spiros Papadimitriou,Jimeng Sun,Christos Faloutsos,Philip S. Yu作者: barium-study 時(shí)間: 2025-3-26 01:28
Adriana Pietramala,Veronica L. Policicchio,Pasquale Rullo,Inderbir Sidhu作者: fulmination 時(shí)間: 2025-3-26 05:03 作者: 吞下 時(shí)間: 2025-3-26 11:44
Conference proceedings 2008tion of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.作者: 責(zé)任 時(shí)間: 2025-3-26 15:57 作者: THE 時(shí)間: 2025-3-26 19:42 作者: Uncultured 時(shí)間: 2025-3-26 23:19 作者: DAFT 時(shí)間: 2025-3-27 03:11 作者: MEAN 時(shí)間: 2025-3-27 06:24
Exceptional Model Miningase as a whole. In classical subgroup discovery, one considers the distribution of a single nominal attribute, and exceptional subgroups show a surprising increase in the occurrence of one of its values. In this paper, we introduce . (EMM), a framework that allows for more complicated target concept作者: 無(wú)表情 時(shí)間: 2025-3-27 10:01
A Joint Topic and Perspective Model for Ideological Discoursel discourse has been considered too difficult to undertake. In this paper we propose a statistical model for ideology discourse. By ideology we mean “a set of general beliefs socially shared by a group of people.” For example, Democratic and Republican are two major political ideologies in the Unite作者: 大笑 時(shí)間: 2025-3-27 15:51 作者: GLIB 時(shí)間: 2025-3-27 20:32 作者: 永久 時(shí)間: 2025-3-28 00:33
Fitted Natural Actor-Critic: A New Algorithm for Continuous State-Action MDPsork in [1] to allow for general function approximation and data reuse. We combine the natural actor-critic architecture [1] with a variant of fitted value iteration using importance sampling. The method thus obtained combines the appealing features of both approaches while overcoming their main weak作者: Hemiparesis 時(shí)間: 2025-3-28 06:00
A New Natural Policy Gradient by Stationary Distribution Metriccept of “natural gradient” that takes the Riemannian metric of the parameter space into account. Kakade [2] applied it to policy gradient reinforcement learning, called a natural policy gradient (NPG). Although NPGs evidently depend on the underlying Riemannian metrics, careful attention was not pai作者: 構(gòu)想 時(shí)間: 2025-3-28 08:52 作者: 長(zhǎng)矛 時(shí)間: 2025-3-28 11:41
Improving Classification with Pairwise Constraints: A Margin-Based Approachting whether a pair of examples belongs to a same class or different classes. We introduce a discriminative learning approach that incorporates pairwise constraints into the conventional margin-based learning framework. We also present an efficient algorithm, PCSVM, to solve the pairwise constraint 作者: 同來(lái)核對(duì) 時(shí)間: 2025-3-28 16:06
Metric Learning: A Support Vector Approach-definite programming problem (QSDP) with local neighborhood constraints, which is based on the Support Vector Machine (SVM) framework. The local neighborhood constraints ensure that examples of the same class are separated from examples of different classes by a margin. In addition to providing an 作者: 敵意 時(shí)間: 2025-3-28 19:20
Support Vector Machines, Data Reduction, and Approximate Kernel Matricesch as distributed networking systems are often prohibitively high, resulting in practitioners of SVM learning algorithms having to apply the algorithm on approximate versions of the kernel matrix induced by a certain degree of data reduction. In this paper, we study the tradeoffs between data reduct作者: FADE 時(shí)間: 2025-3-28 23:49
Hierarchical, Parameter-Free Community Discoveryse to look for community hierarchies, with communities- within-communities. Our proposed method, the . finds such communities at multiple levels, with no user intervention, based on information theoretic principles (MDL). More specifically, it partitions the graph into progressively more refined sub作者: FRONT 時(shí)間: 2025-3-29 06:43 作者: 堅(jiān)毅 時(shí)間: 2025-3-29 07:26 作者: 地殼 時(shí)間: 2025-3-29 12:54
Kernel-Based Inductive Transferning, the task is to find a suitable bias for a new dataset, given a set of known datasets. In this paper, we take a kernel-based approach to inductive transfer, that is, we aim at finding a suitable kernel for the new data. In our setup, the kernel is taken from the linear span of a set of predefin作者: 槍支 時(shí)間: 2025-3-29 19:03 作者: penance 時(shí)間: 2025-3-29 23:15
Client-Friendly Classification over Random Hyperplane Hashese are addressing the problem of centrally learning (linear) classification models from data that is distributed on a number of clients, and subsequently deploying these models on the same clients. Our main goal is to balance the accuracy of individual classifiers and different kinds of costs related作者: 小步舞 時(shí)間: 2025-3-30 03:35
Large-Scale Clustering through Functional Embeddingmize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of imp作者: CLAP 時(shí)間: 2025-3-30 04:37
Clustering Distributed Sensor Data Streamstain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose ., a new distributed algorithm which reduces both the dimensi作者: 赦免 時(shí)間: 2025-3-30 11:48
8樓作者: Badger 時(shí)間: 2025-3-30 13:57
8樓作者: Injunction 時(shí)間: 2025-3-30 17:52
8樓作者: Presbycusis 時(shí)間: 2025-3-30 21:41
9樓作者: considerable 時(shí)間: 2025-3-31 04:25
9樓作者: heterogeneous 時(shí)間: 2025-3-31 07:40
9樓作者: dearth 時(shí)間: 2025-3-31 13:11
9樓作者: Archipelago 時(shí)間: 2025-3-31 14:26
10樓作者: Hemiparesis 時(shí)間: 2025-3-31 20:35
10樓作者: Immunotherapy 時(shí)間: 2025-3-31 22:34
10樓作者: galley 時(shí)間: 2025-4-1 03:36
10樓
