標(biāo)題: Titlebook: Machine Learning and Data Mining in Pattern Recognition; 5th International Co Petra Perner Conference proceedings 2007 Springer-Verlag Berl [打印本頁] 作者: bile-acids 時間: 2025-3-21 17:00
書目名稱Machine Learning and Data Mining in Pattern Recognition影響因子(影響力)
書目名稱Machine Learning and Data Mining in Pattern Recognition影響因子(影響力)學(xué)科排名
書目名稱Machine Learning and Data Mining in Pattern Recognition網(wǎng)絡(luò)公開度
書目名稱Machine Learning and Data Mining in Pattern Recognition網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning and Data Mining in Pattern Recognition被引頻次
書目名稱Machine Learning and Data Mining in Pattern Recognition被引頻次學(xué)科排名
書目名稱Machine Learning and Data Mining in Pattern Recognition年度引用
書目名稱Machine Learning and Data Mining in Pattern Recognition年度引用學(xué)科排名
書目名稱Machine Learning and Data Mining in Pattern Recognition讀者反饋
書目名稱Machine Learning and Data Mining in Pattern Recognition讀者反饋學(xué)科排名
作者: Anthem 時間: 2025-3-22 00:12 作者: 旋轉(zhuǎn)一周 時間: 2025-3-22 03:35
Claudio Marrocco,Mario Molinara,Francesco Tortorella作者: Lacunar-Stroke 時間: 2025-3-22 06:11
Longin Jan Latecki,Aleksandar Lazarevic,Dragoljub Pokrajac作者: Monocle 時間: 2025-3-22 12:49 作者: DEFT 時間: 2025-3-22 14:15
Tomoya Sakai,Atsushi Imiya,Takuto Komazaki,Shiomu Hama作者: Ccu106 時間: 2025-3-22 19:38
Data Clustering: User’s Dilemmaupon the existing published techniques. In this talk we will address the following problems: (i) clustering via evidence accumulation, (ii) simultaneous clustering and dimensionality reduction, (iii) clustering under pair-wise constraints, and (iv) clustering with relevance feedback. Experimental re作者: intelligible 時間: 2025-3-22 22:36
An Incremental Fuzzy Decision Tree Classification Method for Mining Data Streamstinuous attribute. Comparing to the method used in VFDTc, it improves from. to . in processing time. 3) Comparing to VFDTc, fVFDT‘s candidate split-test number decrease from. to ..4)Improve the soft discretization method to be used in data streams mining, it overcomes the problem of noise data and i作者: 我沒有強迫 時間: 2025-3-23 04:22
On Applying Dimension Reduction for Multi-labeled Problems problem and analyze how an objective function of LDA can be interpreted in multi-labeled setting. We also propose a LDA algorithm which is effective in a multi-labeled problem. Experimental results demonstrate that by considering multi-labeled structures LDA can achieve computational efficiency and作者: conduct 時間: 2025-3-23 06:17 作者: omnibus 時間: 2025-3-23 12:53
Conference proceedings 2007d data mining. Although it was a challenging program in the late 1990s, the idea has provided new starting points in pattern recognition and has influenced other areas such as cognitive computer vision. For this edition, the Program Committee received 258 submissions from 37 countries (see Fig. 1). 作者: indemnify 時間: 2025-3-23 16:50 作者: 使混合 時間: 2025-3-23 18:32 作者: 青石板 時間: 2025-3-24 00:00
Magnus Ekdahl,Timo Koskiser Farbenumschlag und das Aufh?ren des Aufsch?umens kennzeichnet das Ende des Vorgangs. Man fügt 200 ccm Wasser hinzu und kocht bis zum Verschwinden der gelben D?mpfe. Ein Stück zur H?lfte angefeuchteten Jodkaliumst?rkepapiers (am besten fertig bezogen) darf nicht mehr an der übergangsstelle blau w作者: foreign 時間: 2025-3-24 04:35 作者: Asseverate 時間: 2025-3-24 06:41 作者: BIBLE 時間: 2025-3-24 13:57
Haibin Cheng,Haifeng Chen,Guofei Jiang,Kenji Yoshihira?hrdungen durch künstliche optische Strahlung (§ 3 OStrV) [4] festgelegt und in diesem Kapitel beschrieben. Der Arbeitgeber kann die Gef?hrdungsbeurteilung selbst erstellen oder eine andere fachkundige Person bzw. Dienstleister damit beauftragen. Für die Durchführung der Gef?hrdungsbeurteilung und d作者: RAGE 時間: 2025-3-24 16:18
Wenbo Cao,Robert Haralickuweisen. Der Laserschutzbeauftragte hat den sicheren Betrieb der Laseranlage zu gew?hrleisten und den Arbeitgeber bei der Erstellung der Gef?hrdungsbeurteilung zu unterstützen. Damit der Laserschutzbeauftragte seiner verantwortungsvollen T?tigkeit gerecht werden kann, ist es sinnvoll, ihn mit Weisun作者: Condyle 時間: 2025-3-24 20:46 作者: conformity 時間: 2025-3-24 23:38
On the Combination of Locally Optimal Pairwise Classifiersr exemplary situations are investigated where the respective assumptions of Naive Bayes or the classical Linear Discriminant Analysis (LDA, Fisher, 1936) fail. It is investigated at which degree of violations of the assumptions it may be advantageous to use single methods or a classifier combination by Pairwise Coupling.作者: Feedback 時間: 2025-3-25 05:16
A Clustering Algorithm Based on Generalized Starsrforms previously defined methods and obtains a smaller number of clusters. Since the GStar algorithm is relatively simple to implement and is also efficient, we advocate its use for tasks that require clustering, such as information organization, browsing, topic tracking, and new topic detection.作者: Handedness 時間: 2025-3-25 09:45 作者: Hemoptysis 時間: 2025-3-25 15:09 作者: 合同 時間: 2025-3-25 19:05 作者: 舊式步槍 時間: 2025-3-25 22:57 作者: 矛盾 時間: 2025-3-26 00:18
An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rulesn two different reject rules (the Chow’s and the ROC rule). In particular, the experiments show that the Chow’s rule is inappropriate when the estimates of the a posteriori probabilities are not reliable.作者: 墊子 時間: 2025-3-26 04:31
Outlier Detection with Kernel Density Functionseld a robust local density estimation. Outliers are then detected by comparing the local density of each point to the local density of its neighbors. Our experiments performed on several simulated data sets have demonstrated that the proposed approach can outperform two widely used outlier detection algorithms (LOF and LOCI).作者: 拉開這車床 時間: 2025-3-26 12:01
Critical Scale for Unsupervised Cluster Discoverythat the detected cluster, represented as a mode of the PDF, can be validated by observing the lifetime of the mode in scale space. Statistical properties of the lifetime, however, are unclear. In this paper, we propose a concept of the ‘critical scale’ and explore perspectives on handling it for the cluster validation.作者: chassis 時間: 2025-3-26 14:27
Minimum Information Loss Cluster Analysis for Categorical Data. For this reason we use the latent class model only to define a set of “elementary” classes by estimating a mixture of a large number components. We propose a hierarchical “bottom up” cluster analysis based on unifying the elementary latent classes sequentially. The clustering procedure is controlled by minimum information loss criterion.作者: 猛烈責(zé)罵 時間: 2025-3-26 19:02
On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiersby showing explicitly that, depending on context, independent (’Na?ve’) classifiers can be as bad as tossing coins. Regardless of this, independence may beat the generating model in learning supervised classification and we explicitly provide one such scenario.作者: Foam-Cells 時間: 2025-3-26 22:51
Comparison of a Novel Combined ECOC Strategy with Different Multiclass Algorithms Together with Paragorithms (e.g. the regularization parameter . and the kernel parameter .). We tested different parameter optimization methods on different learning algorithms and confirmed the better performance of .versus ., which can be explained by the maximum margin approach of SVMs.作者: FLEET 時間: 2025-3-27 03:06
Affine Feature Extraction: A Generalization of the Fukunaga-Koontz Transformationus can be solved very efficiently. Also we investigate the information-theoretical properties of the new method and study the relationship of our method with other methods. The experimental results show that our method, as PCA and FDA, can be used as another preliminary data-exploring tool to help solve machine learning and data mining problems.作者: Diluge 時間: 2025-3-27 08:27 作者: 女歌星 時間: 2025-3-27 10:45 作者: 外形 時間: 2025-3-27 15:18 作者: 變異 時間: 2025-3-27 18:43 作者: 牛馬之尿 時間: 2025-3-28 00:55
Magnus Ekdahl,Timo Koskitt zu entfernen, hebt es mit einer Pinzette heraus, schüttelt den anhaftenden ?ther ab und trocknet im Exsikkator 25 Minuten. Den gebrauchten ?ther gie?t man als,,Spül?ther“ in eine kenntlich gemachte Flasche, um ihn sp?ter zum gleichen Zwecke zu benutzen. Der gewogene Blumendraht wird in einen Me?k作者: DEFER 時間: 2025-3-28 05:26 作者: 剝皮 時間: 2025-3-28 06:54
Gero Szepannek,Bernd Bischl,Claus Weihst sich in einem durchsichtigen K?rper, wie Luft, geradlinig nach allen Richtungen fort Man kann das leicht beobachten, wenn in einen verdunkelten Raum durch eine enge ?ffnung Sonnenstrahlen einfallen, an dem Staub sieht man die Richtung der Lichtstrahlen deutlich. Daraus erkl?rt sich auch, da? undur作者: 依法逮捕 時間: 2025-3-28 11:33
Haibin Cheng,Haifeng Chen,Guofei Jiang,Kenji YoshihiraSie beruht auf dem Arbeitsschutzgesetz (ArbSchG) [2] und der Unfallverhütungsvorschrift Grunds?tze der Pr?vention (DGUV Vorschrift 1) [3], nach denen alle Arbeitgeber verpflichtet sind, für alle vorhandenen Arbeitspl?tze eine Gef?hrdungsbeurteilung durchzuführen. § 5 ArbSchG beschreibt Gefahrenursac作者: deficiency 時間: 2025-3-28 18:36 作者: Fresco 時間: 2025-3-28 20:29
Data Clustering: User’s Dilemmanumber of diverse disciplines. The goal is to partition a set of n d-dimensional points into k clusters, where k may or may not be known. Most clustering techniques require the definition of a similarity measure between patterns, which is not easy to specify in the absence of any prior knowledge abo作者: Ascendancy 時間: 2025-3-29 01:37
On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers inconclusive results about their impact. Some theoretical understanding of when they work is available, but a definite answer seems to be lacking. This paper derives distributions that maximizes the statewise difference to the respective product of marginals. These distributions are, in a sense the作者: Parabola 時間: 2025-3-29 04:09
Comparison of a Novel Combined ECOC Strategy with Different Multiclass Algorithms Together with Parare is much need for improvements in the field of multiclass learning. We developed a novel combination algorithm called ., which is based on posterior class probabilities. It assigns, according to the Bayesian rule, the respective instance to the class with the highest posterior probability. A probl作者: endure 時間: 2025-3-29 10:13
Multi-source Data Modelling: Integrating Related Data to Improve Model Performanceess the lack of model performance for data from which it is difficult to extract relationships. This paper proposes a set of algorithms that allow the integration of data from multiple datasets that are related, as well as results from the implementation of these techniques using data from the field作者: albuminuria 時間: 2025-3-29 12:25
An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rulesst for a wrong classification can be so high that can be convenient to avoid a decision and reject the sample. This paper presents a comparison between two different reject rules (the Chow’s and the ROC rule). In particular, the experiments show that the Chow’s rule is inappropriate when the estimat作者: institute 時間: 2025-3-29 17:17 作者: Ophthalmoscope 時間: 2025-3-29 21:09 作者: SOB 時間: 2025-3-30 01:18 作者: 常到 時間: 2025-3-30 05:48 作者: conduct 時間: 2025-3-30 09:00
An Agent-Based Approach to the Multiple-Objective Selection of Reference Vectorselection procedures are of vital importance to machine learning and data mining. The suggested approach is based on the multiple agent paradigm. The authors propose using JABAT middleware as a tool and the original instance reduction procedure as a method for selecting reference vectors under multip作者: 啪心兒跳動 時間: 2025-3-30 13:01
On Applying Dimension Reduction for Multi-labeled Problems problem such as text categorization, data samples can belong to multiple classes and the task is to output a set of class labels associated with new unseen data sample. As common in text categorization problem, learning a classifier in a high dimensional space can be difficult, known as the curse o作者: Kidnap 時間: 2025-3-30 18:19
Nonlinear Feature Selection by Relevance Feature Vector Machinenear dependencies between features. However it is known that the number of support vectors required in SVM typically grows linearly with the size of the training data set. Such a limitation of SVM becomes more critical when we need to select a small subset of relevant features from a very large numb作者: Manifest 時間: 2025-3-30 21:02
Affine Feature Extraction: A Generalization of the Fukunaga-Koontz Transformations principal component analysis (.), Fisher’s linear discriminant analysis (.), et al. In this paper, we describe a novel feature extraction method for binary classification problems. Instead of finding linear subspaces, our method finds lower- dimensional affine subspaces for data observations. Our 作者: TIA742 時間: 2025-3-31 02:27 作者: 關(guān)心 時間: 2025-3-31 08:16 作者: JOG 時間: 2025-3-31 11:48
Kernel MDL to Determine the Number of Clusters Kernel MDL (KMDL), is particularly adapted to the use of kernel K-means clustering algorithm. Its formulation is based on the definition of MDL derived for Gaussian Mixture Model (GMM). We demonstrate the efficiency of our approach on both synthetic data and real data such as SPOT5 satellite images作者: Obstreperous 時間: 2025-3-31 16:48
Critical Scale for Unsupervised Cluster Discoverya points based on the estimation of probability density function (PDF) using a Gaussian kernel with a variable scale parameter. It has been suggested that the detected cluster, represented as a mode of the PDF, can be validated by observing the lifetime of the mode in scale space. Statistical proper作者: Fulsome 時間: 2025-3-31 19:37 作者: 在前面 時間: 2025-3-31 23:29
A Clustering Algorithm Based on Generalized Starsithm proposed by Aslam ., and recently improved by them and other researchers. In this method we introduced a new concept of star allowing a different star-shaped form with better overlapping clusters. The evaluation experiments on standard document collections show that the proposed algorithm outpe作者: Coma704 時間: 2025-4-1 05:00 作者: 擁護(hù)者 時間: 2025-4-1 08:00
Machine Learning and Data Mining in Pattern Recognition978-3-540-73499-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 蝕刻術(shù) 時間: 2025-4-1 10:49
https://doi.org/10.1007/978-3-540-73499-4Spam; classification; cognition; data mining; learning; machine learning; pattern recognition
