標(biāo)題: Titlebook: Classification - the Ubiquitous Challenge; Proceedings of the 2 Claus Weihs,Wolfgang Gaul Conference proceedings 2005 Springer-Verlag Berli [打印本頁] 作者: Coagulant 時(shí)間: 2025-3-21 17:53
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作者: indices 時(shí)間: 2025-3-21 22:00
Multimedia Pattern Recognition in Soccer Video Using Time Intervalssoccer video, we compare three different machine learning techniques, i.c. C4.5 decision tree, Maximum Entropy, and Support Vector Machine. It was found that by using the TIME framework the amount of video a user has to watch in order to see almost all highlights can be reduced considerably, especially in combination with a Support Vector Machine.作者: 壯觀的游行 時(shí)間: 2025-3-22 02:15
Quantitative Assessment of the Responsibility for the Disease Load in a Populatione concept of partial . has been developed. The partial . offers a unique solution for allocating shares of . to a number of exposure factors of interest, as illustrated by data from the German G?ttingen Risk, Incidence, and Prevalence Study (G.R.I.P.S.).作者: 強(qiáng)行引入 時(shí)間: 2025-3-22 06:23
Bagging, Boosting and Ordinal Classificationnts of bagging and boosting, which make use of the ordinal structure and it is shown how the predictive power might be improved. Comparisons are based not only on misclassification rates but also on general distance measures, which reflect the difference between true and predicted class.作者: Feckless 時(shí)間: 2025-3-22 09:09 作者: GROG 時(shí)間: 2025-3-22 16:48
Iterative Majorization Approach to the Distance-based Discriminant Analysislems, and can be applied as a dimensionality reduction technique. In the latter case, the number of necessary discriminative dimensions can be determined exactly. The sought transformation is found as a solution to an optimization problem using iterative majorization.作者: GROG 時(shí)間: 2025-3-22 19:28 作者: left-ventricle 時(shí)間: 2025-3-23 01:15
What Drives Serendipity Research?,ng the jack-knife procedure, and by Kiers (2004) for CP and Tucker3 analysis using the bootstrap procedure. The present paper reviews the latter procedures, discusses their performance as reported by Kiers (2004), and illustrates them on an example data set.作者: cataract 時(shí)間: 2025-3-23 04:18 作者: 地名表 時(shí)間: 2025-3-23 08:18
Lisa N. Fink,J. César Félix-Brasdefere concept of partial . has been developed. The partial . offers a unique solution for allocating shares of . to a number of exposure factors of interest, as illustrated by data from the German G?ttingen Risk, Incidence, and Prevalence Study (G.R.I.P.S.).作者: 灰姑娘 時(shí)間: 2025-3-23 11:19
https://doi.org/10.1057/9781137373953nts of bagging and boosting, which make use of the ordinal structure and it is shown how the predictive power might be improved. Comparisons are based not only on misclassification rates but also on general distance measures, which reflect the difference between true and predicted class.作者: PATRI 時(shí)間: 2025-3-23 15:36 作者: hurricane 時(shí)間: 2025-3-23 19:56
Researching Subcultures, Myth and Memorylems, and can be applied as a dimensionality reduction technique. In the latter case, the number of necessary discriminative dimensions can be determined exactly. The sought transformation is found as a solution to an optimization problem using iterative majorization.作者: Congeal 時(shí)間: 2025-3-24 01:42 作者: Encapsulate 時(shí)間: 2025-3-24 05:36 作者: amenity 時(shí)間: 2025-3-24 07:52 作者: 帶來 時(shí)間: 2025-3-24 12:32 作者: Malcontent 時(shí)間: 2025-3-24 18:16
Classification and Data Mining in Musicologyledge in musicology and hence to filter out information that is relevant from the point of view of music theory. This is illustrated by a number of examples from classical music, including the analysis of scores and of musical performance.作者: Vulnerary 時(shí)間: 2025-3-24 19:19 作者: 原始 時(shí)間: 2025-3-24 23:27
Expectation of Random Sets and the ‘Mean Values’ of Interval DataRCRs) are defined and the properties of different definitions for expectations of RCSs, applied on RCRs are studied. In addition known mean values of interval data are integrated in this generalized approach.作者: Detoxification 時(shí)間: 2025-3-25 05:24 作者: 無效 時(shí)間: 2025-3-25 10:14
Researching Sociopragmatic Variabilityd both its parameters and the optimal subset of features extracted from the images are optimized jointly in the framework of a wrapper optimization. The search for an optimal subset of features is performed using a range of different sequential and parallel search strategies including genetic algorithms.作者: 擴(kuò)音器 時(shí)間: 2025-3-25 14:11 作者: Humble 時(shí)間: 2025-3-25 17:40 作者: ineptitude 時(shí)間: 2025-3-25 22:41 作者: debris 時(shí)間: 2025-3-26 02:10 作者: JOT 時(shí)間: 2025-3-26 07:56
Bayesian Mixed Membership Models for Soft Clustering and Classification where we explore types of disability; (ii) abstracts and bibliographies from articles published in .. In the first application we use a Monte Carlo Markov chain implementation for sampling from the posterior distribution. In the second application, because of the size and complexity of the data bas作者: dithiolethione 時(shí)間: 2025-3-26 10:09
Organising the Knowledge Space for Software Componentsy, thesaurus, conceptual model, and logical framework functions. Focal point is an axiomatised ontology that, in addition to the usual static view on knowledge, also intrinsically addresses the dynamics, i.e. the behaviour of software. Modal logics are central here — providing a bridge between class作者: 擴(kuò)張 時(shí)間: 2025-3-26 14:15 作者: urethritis 時(shí)間: 2025-3-26 20:06
What Drives Serendipity Research?,y, thesaurus, conceptual model, and logical framework functions. Focal point is an axiomatised ontology that, in addition to the usual static view on knowledge, also intrinsically addresses the dynamics, i.e. the behaviour of software. Modal logics are central here — providing a bridge between class作者: Hypopnea 時(shí)間: 2025-3-26 23:28 作者: 涂掉 時(shí)間: 2025-3-27 03:43 作者: 分開如此和諧 時(shí)間: 2025-3-27 05:46
Claus Weihs,Wolfgang GaulIncludes supplementary material: 作者: obsession 時(shí)間: 2025-3-27 13:31 作者: 寬容 時(shí)間: 2025-3-27 17:22 作者: 通情達(dá)理 時(shí)間: 2025-3-27 21:50
Cluster Ensemblespplications. Aggregating these to a “common” solution amounts to finding a consensus clustering, which can be characterized in a general optimization framework. We discuss recent conceptual and computational advances in this area, and indicate how these can be used for analyzing the structure in cluster ensembles by clustering its elements.作者: 有害 時(shí)間: 2025-3-28 00:12 作者: 他很靈活 時(shí)間: 2025-3-28 05:17 作者: Matrimony 時(shí)間: 2025-3-28 06:21 作者: 有斑點(diǎn) 時(shí)間: 2025-3-28 10:52
https://doi.org/10.1007/978-3-030-25157-4 assumptions on four levels: population, subject, latent variable, and sampling scheme. Population level assumptions describe the general structure of the population that is common to all subjects. Subject level assumptions specify the distribution of observable responses given individual membership作者: Stress 時(shí)間: 2025-3-28 17:07 作者: 閑蕩 時(shí)間: 2025-3-28 18:48 作者: 競選運(yùn)動(dòng) 時(shí)間: 2025-3-29 00:36 作者: Itinerant 時(shí)間: 2025-3-29 05:15
https://doi.org/10.1007/978-3-319-31954-4pplications. Aggregating these to a “common” solution amounts to finding a consensus clustering, which can be characterized in a general optimization framework. We discuss recent conceptual and computational advances in this area, and indicate how these can be used for analyzing the structure in clu作者: Silent-Ischemia 時(shí)間: 2025-3-29 07:55 作者: cinder 時(shí)間: 2025-3-29 11:25
What Drives Serendipity Research?,n of software components from component repositories and the development of components for these repositories requires an accessible information infrastructure that allows the description and comparison of these components..General knowledge relating to software development is equally important in t作者: 夾克怕包裹 時(shí)間: 2025-3-29 16:41 作者: WATER 時(shí)間: 2025-3-29 22:45
Lisa N. Fink,J. César Félix-Brasdeferfactor. While valid approaches to the estimation of crude or adjusted . exist, a problem remains concerning the attribution of . to each of a set of several exposure factors. Inspired by mathematical game theory, namely, the axioms of fairness and the Shapley value, introduced by Shapley in 1953, th作者: Accommodation 時(shí)間: 2025-3-30 00:06
María J. Barros García,Marina Terkourafiintervals for the parameters of this model, based on parametric and nonparametric bootstrap. Moreover, the label-switching problem is discussed and a solution to handle it introduced. The results are illustrated using a well-known dataset.作者: 合同 時(shí)間: 2025-3-30 06:28
Researching Sociopragmatic Variabilityon sampling cases from the training set, or changing weights for cases. Reduction of classification error can also be achieved by random selection of variables to the training subsamples or directly to the model. In this paper we propose a method of feature selection for ensembles that significantly作者: 退潮 時(shí)間: 2025-3-30 09:59
Researching Sociopragmatic Variability high-speed camera, and the task is complicated by the fact that very high sensitivity is required in spite of a highly dynamic / noisy background and that large amounts of data need to be processed online. In a first stage, individual images are rated and these results are then aggregated in a seco作者: RENAL 時(shí)間: 2025-3-30 15:32
https://doi.org/10.1057/9781137373953t of them have in common that the class indicator is treated as a nominal response without any structure. Since in many practical situations the class must be considered as an ordered categorical variable, it seems worthwhile to take this additional information into account. We propose several varia作者: Offbeat 時(shí)間: 2025-3-30 17:31
https://doi.org/10.1057/9781137373953thodology to explore two aspects of a cluster found by any cluster analysis method: the cluster should be separated from the rest of the data, and the points of the cluster should not split up into further separated subclasses. Both aspects can be visually assessed by linear projections of the data 作者: 有法律效應(yīng) 時(shí)間: 2025-3-30 21:41
https://doi.org/10.1057/9781137373953ost and Arc-x(.). While belonging to the same algorithms family, they differ in the way of combining classifiers. Adaboost uses weighted majority vote while Arc-x(.) combines them through simple majority vote. Breiman (1998) obtains the best results for Arc-x(.) with . = 4 but higher values were not作者: Prophylaxis 時(shí)間: 2025-3-31 01:13
Researching Subcultures, Myth and Memoryhesis and its inverse. The problem formulation relies on inter-observation distances only, which is shown to improve non-parametric and non-linear classifier performance on benchmark and real-world data sets. The proposed approach is suitable for both binary and multiple-category classification prob作者: 袖章 時(shí)間: 2025-3-31 07:48 作者: 格子架 時(shí)間: 2025-3-31 09:24
Myth and Authenticity in Subculture Studiess to their extremal points p-dimensional intervals (rectangles) are treated as Random Closed Sets (RCSs). In this framework Random Closed Rectangles (RCRs) are defined and the properties of different definitions for expectations of RCSs, applied on RCRs are studied. In addition known mean values of 作者: 闖入 時(shí)間: 2025-3-31 13:27
Classification - the Ubiquitous Challenge978-3-540-28084-2Series ISSN 1431-8814 Series E-ISSN 2198-3321 作者: happiness 時(shí)間: 2025-3-31 20:59
1431-8814 Overview: Includes supplementary material: 978-3-540-25677-9978-3-540-28084-2Series ISSN 1431-8814 Series E-ISSN 2198-3321 作者: 用手捏 時(shí)間: 2025-3-31 23:29
Stages in the Acquisition of Foreign Lexisnd authors are analyzed as to their word length. It is shown that word length is an important factor in the synergetic self-regulation of texts and text types, and that word length may significantly contribute to a new typology of discourse types.作者: micronized 時(shí)間: 2025-4-1 02:26
https://doi.org/10.1007/978-3-319-31954-4pplications. Aggregating these to a “common” solution amounts to finding a consensus clustering, which can be characterized in a general optimization framework. We discuss recent conceptual and computational advances in this area, and indicate how these can be used for analyzing the structure in cluster ensembles by clustering its elements.作者: 初次登臺 時(shí)間: 2025-4-1 09:42 作者: airborne 時(shí)間: 2025-4-1 12:00 作者: crucial 時(shí)間: 2025-4-1 18:17 作者: 搬運(yùn)工 時(shí)間: 2025-4-1 19:46
Bayesian Mixed Membership Models for Soft Clustering and Classification assumptions on four levels: population, subject, latent variable, and sampling scheme. Population level assumptions describe the general structure of the population that is common to all subjects. Subject level assumptions specify the distribution of observable responses given individual membership作者: Amorous 時(shí)間: 2025-4-2 01:59
Predicting Protein Secondary Structure with Markov Modelslices or coils. Spacial and other properties are described by the higher order structures. The classification task we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown pr
