標(biāo)題: Titlebook: Discovery Science; 16th International C Johannes Fürnkranz,Eyke Hüllermeier,Tomoyuki Higuc Conference proceedings 2013 Springer-Verlag Berl [打印本頁] 作者: invigorating 時間: 2025-3-21 17:29
書目名稱Discovery Science影響因子(影響力)
書目名稱Discovery Science影響因子(影響力)學(xué)科排名
書目名稱Discovery Science網(wǎng)絡(luò)公開度
書目名稱Discovery Science網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Discovery Science被引頻次
書目名稱Discovery Science被引頻次學(xué)科排名
書目名稱Discovery Science年度引用
書目名稱Discovery Science年度引用學(xué)科排名
書目名稱Discovery Science讀者反饋
書目名稱Discovery Science讀者反饋學(xué)科排名
作者: 老巫婆 時間: 2025-3-21 22:44
Model Tree Ensembles for Modeling Dynamic Systems,ally converted into a classical regression problem, which can then be solved with any nonlinear regression approach. As tree ensembles are a very successful predictive modelling approach, we investigate the use of tree ensembles for regression for this task..While ensembles of regression trees have 作者: Arb853 時間: 2025-3-22 03:07
Fast and Scalable Image Retrieval Using Predictive Clustering Trees,ficient and accurate systems for image retrieval. State-of-the-art systems for image retrieval use the bag-of-visual-words representation of the images. However, the computational bottleneck in all such systems is the construction of the visual vocabulary (i.e., how to obtain the visual words). This作者: Tdd526 時間: 2025-3-22 06:03
Avoiding Anomalies in Data Stream Learning, refer to data cleaning as a pre-processing before the learning task. The problem of data cleaning is exacerbated when learning in the computational model of data streams. In this paper we present a streaming algorithm for learning classification rules able to detect contextual anomalies in the data作者: Freeze 時間: 2025-3-22 09:16
Generalizing from Example Clusters,t with the given clusters. This is essentially a semi-supervised clustering problem, but it differs from previously studied semi-supervised clustering settings in significant ways. Earlier work has shown that none of the existing methods for semi-supervised clustering handle this problem well. We id作者: 隨意 時間: 2025-3-22 16:18 作者: 隨意 時間: 2025-3-22 20:25 作者: debouch 時間: 2025-3-23 01:08
A New Approach to String Pattern Mining with Approximate Match,ring data analysis on strings such as texts, word sequences, and genome sequences. The problem becomes difficult if the string patterns are allowed to match approximately, i.e., a fixed number of errors leads to an explosion in the number of small solutions, and a fixed ratio of errors violates the 作者: NAVEN 時間: 2025-3-23 04:46
OntoDM-KDD: Ontology for Representing the Knowledge Discovery Process,-KDD defines the most essential entities for describing data mining investigations in the context of KD in a two-layered ontological structure. The ontology is aligned and reuses state-of-the-art resources for representing scientific investigations, such as Information Artifact Ontology (IAO) and On作者: GNAW 時間: 2025-3-23 09:35 作者: 絕食 時間: 2025-3-23 09:43
Multi-interval Discretization of Continuous Attributes for Label Ranking,en a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consid作者: nocturia 時間: 2025-3-23 14:16
Identifying Super-Mediators of Information Diffusion in Social Networks,an important role in receiving the information and passing it to other nodes. We mathematically formulate this as a difference maximization problem in the average influence degree with respect to a node removal, ., a node that contributes to making the difference large is influential. We further cha作者: 神化怪物 時間: 2025-3-23 21:29 作者: Excise 時間: 2025-3-23 23:23 作者: 蜿蜒而流 時間: 2025-3-24 04:10
Mining Interesting Patterns in Multi-relational Data with N-ary Relationships,e a new pattern syntax for such data, develop an efficient algorithm for mining it, and define a suitable interestingness measure that is able to take into account prior information of the data miner. Our approach is a strict generalisation of prior work on multi-relational data in which relationshi作者: Evolve 時間: 2025-3-24 08:22
Learning Hierarchical Multi-label Classification Trees from Network Data,ltiple classes at the same time and consider the hierarchical organization of the classes. It assumes that the instances are placed in a network and uses information on the network connections during the learning of the predictive model. Many real world prediction problems have classes that are orga作者: 高射炮 時間: 2025-3-24 13:02 作者: Ointment 時間: 2025-3-24 17:30 作者: 思考才皺眉 時間: 2025-3-24 21:48
Fast Compression of Large-Scale Hypergraphs for Solving Combinatorial Problems,ses multikey Quicksort given by Bentley and Sedgewick. By conducting experiments with various datasets, we show that our algorithm is significantly faster and requires much smaller memory than an existing method.作者: poliosis 時間: 2025-3-25 02:33
Discovery Science978-3-642-40897-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: FLORA 時間: 2025-3-25 03:57 作者: Libido 時間: 2025-3-25 09:36 作者: Hyperopia 時間: 2025-3-25 12:36
On Going Out and the Experience of Students single analysis. Most models and methods can only model a single data resolution, that is, vectors of the same dimensionality, at a time. This is also true for mixture models, the model of interest. In this paper, we propose a multiresolution mixture model capable of modeling data in multiple resol作者: intrude 時間: 2025-3-25 19:14 作者: Carbon-Monoxide 時間: 2025-3-25 23:03 作者: Medicaid 時間: 2025-3-26 02:10 作者: 陰謀 時間: 2025-3-26 05:45
I was the Shy and Awkward Girl Mariyaht with the given clusters. This is essentially a semi-supervised clustering problem, but it differs from previously studied semi-supervised clustering settings in significant ways. Earlier work has shown that none of the existing methods for semi-supervised clustering handle this problem well. We id作者: Ballerina 時間: 2025-3-26 11:07 作者: demote 時間: 2025-3-26 15:39 作者: 不近人情 時間: 2025-3-26 20:47
https://doi.org/10.1007/978-3-319-32040-3ring data analysis on strings such as texts, word sequences, and genome sequences. The problem becomes difficult if the string patterns are allowed to match approximately, i.e., a fixed number of errors leads to an explosion in the number of small solutions, and a fixed ratio of errors violates the 作者: NIL 時間: 2025-3-27 00:49
https://doi.org/10.1007/978-1-4612-4970-2-KDD defines the most essential entities for describing data mining investigations in the context of KD in a two-layered ontological structure. The ontology is aligned and reuses state-of-the-art resources for representing scientific investigations, such as Information Artifact Ontology (IAO) and On作者: 伴隨而來 時間: 2025-3-27 01:56
W. Fisher Cassie,T. Constantinetional database into a corpus of documents. Wordification aims at producing simple, easy to understand features, acting as words in the transformed Bag-Of-Words representation. As in other propositionalization methods, after the wordification step any propositional data mining algorithm can be appli作者: 懸崖 時間: 2025-3-27 09:12
https://doi.org/10.1007/978-3-663-05106-0en a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consid作者: 皮薩 時間: 2025-3-27 12:31
Legistische Richtlinien in ?sterreichan important role in receiving the information and passing it to other nodes. We mathematically formulate this as a difference maximization problem in the average influence degree with respect to a node removal, ., a node that contributes to making the difference large is influential. We further cha作者: Lasting 時間: 2025-3-27 16:46
Studien zu einer Theorie der Gesetzgebung . and .. We propose the SM2D modular data driven approach that provides predictive models for each sub-process of a global hydrological process. We show that this solution improves the predictive accuracy regarding a global approach. The originality of our proposition is threefold: (1) the predicti作者: 不公開 時間: 2025-3-27 21:24
Ein Rechtsinformationssystem für ?sterreich for exact learning of chain event graphs from multivariate data. While the exact algorithm is slow, it allows reasonably fast approximations and provides clues for implementing more scalable heuristic algorithms.作者: 明確 時間: 2025-3-28 01:35 作者: paradigm 時間: 2025-3-28 05:33 作者: dainty 時間: 2025-3-28 06:24
https://doi.org/10.1007/978-3-663-15806-6isolating a group of outliers, i.e. rare events representing micro-clusters of less – or significantly less – than 1% of the whole dataset. This research issue is critical for example in medical applications. The problem is difficult to handle as it lies at the frontier between outlier detection and作者: Frenetic 時間: 2025-3-28 12:10 作者: 并入 時間: 2025-3-28 15:39 作者: reperfusion 時間: 2025-3-28 21:18
Johannes Fürnkranz,Eyke Hüllermeier,Tomoyuki HigucConference proceedings of the International Conference on Discovery Science, DS 2013作者: Lasting 時間: 2025-3-29 02:26 作者: Obliterate 時間: 2025-3-29 04:27
https://doi.org/10.1007/978-3-642-40897-7constraint-based clustering; domain ontology; hypernetworks; semantic data mining; structure learning; al作者: Parameter 時間: 2025-3-29 08:06 作者: Complement 時間: 2025-3-29 14:18 作者: 自然環(huán)境 時間: 2025-3-29 17:23
Fast Compression of Large-Scale Hypergraphs for Solving Combinatorial Problems,ses multikey Quicksort given by Bentley and Sedgewick. By conducting experiments with various datasets, we show that our algorithm is significantly faster and requires much smaller memory than an existing method.作者: Hallowed 時間: 2025-3-29 23:10
Mining Interesting Patterns in Multi-relational Data with N-ary Relationships,ps were restricted to be binary, as well as of prior work on local pattern mining from a single .-ary relationship. Remarkably, despite being more general our algorithm is comparably fast or faster than the state-of-the-art in these less general problem settings.作者: 停止償付 時間: 2025-3-30 00:50
Inductive Process Modeling of Rab5-Rab7 Conversion in Endocytosis,st introduce a formal representation of the domain knowledge for modeling this process. We then present the design of the IPM experiments using the domain knowledge and measured data and the results obtained from these experiments. We finally compare our results with results already published in the literature.作者: 黑豹 時間: 2025-3-30 06:48 作者: RAFF 時間: 2025-3-30 10:21 作者: dyspareunia 時間: 2025-3-30 14:28 作者: 冷淡周邊 時間: 2025-3-30 19:22 作者: 受人支配 時間: 2025-3-30 23:26
W. Fisher Cassie,T. Constantinef attributes times the number of examples. The paper presents the wordification methodology, implemented in a cloud-based web data mining platform Clowd-Flows, and describes the experiments in two real-life datasets together with a critical comparison to the RSD propositionalization approach.作者: LATHE 時間: 2025-3-31 01:19 作者: 報復(fù) 時間: 2025-3-31 06:00 作者: 反抗者 時間: 2025-3-31 11:06
OntoDM-KDD: Ontology for Representing the Knowledge Discovery Process,DD supports the annotation of DM investigations in application domains. The ontology has been thoroughly assessed following the best practices in ontology engineering, is fully interoperable with many domain resources and easily extensible. OntoDM-KDD is available at ..作者: Spangle 時間: 2025-3-31 15:59
A Wordification Approach to Relational Data Mining,f attributes times the number of examples. The paper presents the wordification methodology, implemented in a cloud-based web data mining platform Clowd-Flows, and describes the experiments in two real-life datasets together with a critical comparison to the RSD propositionalization approach.作者: Obligatory 時間: 2025-3-31 19:41
0302-9743 ational Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances 作者: 慢慢流出 時間: 2025-4-1 00:34
