標題: Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Peter A. Flach,Tijl Bie,Nello Cristianini Conference proceeding [打印本頁] 作者: SCOWL 時間: 2025-3-21 18:28
書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)
書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學科排名
書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度
書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度學科排名
書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次
書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學科排名
書目名稱Machine Learning and Knowledge Discovery in Databases年度引用
書目名稱Machine Learning and Knowledge Discovery in Databases年度引用學科排名
書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋
書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學科排名
作者: 脆弱帶來 時間: 2025-3-22 00:10
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620490.jpg作者: 愉快么 時間: 2025-3-22 00:37 作者: 彎彎曲曲 時間: 2025-3-22 06:16 作者: overwrought 時間: 2025-3-22 09:10 作者: 因無茶而冷淡 時間: 2025-3-22 15:46 作者: concise 時間: 2025-3-22 20:44
Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methodshis leads to fast algorithms that are applicable to large-scale problems. We empirically analyze the performances of these tree-based learners combined or not with the feature generation capability on several standard datasets.作者: 詞匯 時間: 2025-3-22 23:13 作者: AV-node 時間: 2025-3-23 03:53
Discovering Descriptive Tile Treesthis paper is that we find the optimal tile in only Θ(. min(.,.)) time. Stijl can either be employed as a top-. miner, or by MDL we can identify the tree that describes the data best..Experiments show we find succinct models that accurately summarise the data, and, by their hierarchical property are easily interpretable.作者: Range-Of-Motion 時間: 2025-3-23 06:02 作者: 挑剔為人 時間: 2025-3-23 13:45 作者: Explicate 時間: 2025-3-23 17:13
Label-Noise Robust Logistic Regression and Its Applicationsaluate the performance of our approach in synthetic experiments and we demonstrate several real applications including gene expression analysis, class topology discovery and learning from crowdsourcing data.作者: Nomadic 時間: 2025-3-23 19:57
Sentiment Classification with Supervised Sequence Embeddingpresent comparative evaluations of this method using two large-scale datasets for sentiment classification in online reviews (Amazon and TripAdvisor). The proposed method outperforms standard baselines that rely on bag-of-words representation populated with .-gram features.作者: 少量 時間: 2025-3-23 22:22 作者: Project 時間: 2025-3-24 04:06 作者: deface 時間: 2025-3-24 07:32
Conference proceedings 2012y in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track inclu作者: 反對 時間: 2025-3-24 13:10 作者: arabesque 時間: 2025-3-24 18:44
g to telescopes – including trying to design a perfect telescope by means of mathematical theory – his dioptrics is significant for our understanding of seventeenth-century relations between theory and practice978-90-481-6706-7978-1-4020-2698-0Series ISSN 1385-0180 Series E-ISSN 2215-0064 作者: exquisite 時間: 2025-3-24 22:10
Luc De Raedtg to telescopes – including trying to design a perfect telescope by means of mathematical theory – his dioptrics is significant for our understanding of seventeenth-century relations between theory and practice978-90-481-6706-7978-1-4020-2698-0Series ISSN 1385-0180 Series E-ISSN 2215-0064 作者: Demonstrate 時間: 2025-3-25 00:07
Pieter Abbeelry of light. As Huygens applied his mathematical proficiency to practical issues pertaining to telescopes – including trying to design a perfect telescope by means of mathematical theory – his dioptrics is significant for our understanding of seventeenth-century relations between theory and practice作者: 拋物線 時間: 2025-3-25 05:48 作者: 非秘密 時間: 2025-3-25 09:40
Daniel Keimanus-like keratosis. Immunohistochemical stains, such as the nuclear marker MITF or SOX10, may be a helpful ancillary diagnostic test in evaluating the density and growth pattern of melanocytes. Immunohistochemical stains may also help to identify associated invasive melanoma, especially if the inva作者: 為敵 時間: 2025-3-25 12:09
Padhraic Smythanus-like keratosis. Immunohistochemical stains, such as the nuclear marker MITF or SOX10, may be a helpful ancillary diagnostic test in evaluating the density and growth pattern of melanocytes. Immunohistochemical stains may also help to identify associated invasive melanoma, especially if the inva作者: 未完成 時間: 2025-3-25 18:05 作者: 蒼白 時間: 2025-3-25 20:09
Matteo Riondato,Eli Upfalanus-like keratosis. Immunohistochemical stains, such as the nuclear marker MITF or SOX10, may be a helpful ancillary diagnostic test in evaluating the density and growth pattern of melanocytes. Immunohistochemical stains may also help to identify associated invasive melanoma, especially if the inva作者: 不確定 時間: 2025-3-26 01:40
Arno Siebes,René Kerstenanus-like keratosis. Immunohistochemical stains, such as the nuclear marker MITF or SOX10, may be a helpful ancillary diagnostic test in evaluating the density and growth pattern of melanocytes. Immunohistochemical stains may also help to identify associated invasive melanoma, especially if the inva作者: 情感 時間: 2025-3-26 06:48
Maxime Gasse,Alex Aussem,Haytham Elghazelygonal structures such as rhomboids. In addition to helping with the primary diagnosis of LMM, dermoscopy is used to optimize biopsy site selection, to select treatment margins, and to identify potential recurrence during post-treatment monitoring..Lentigo maligna/lentigo maligna melanoma (LM/LMM) u作者: FOLD 時間: 2025-3-26 10:11 作者: 有常識 時間: 2025-3-26 15:11 作者: PATHY 時間: 2025-3-26 17:42 作者: 愛好 時間: 2025-3-26 23:12
ürün Dogan,Tobias Glasmachers,Christian Igelr gene in transduced human monocyte (U937) cell lines was investigated. Expression of the transgene was driven by the spleen focus-forming virus (SFFV) LTRs. Transduction efficiency was studied using both the IDLV (ID-SFFV-GFP) and their wild-type counterparts (integrase-proficient SFFV-GFP). GFP ex作者: JOT 時間: 2025-3-27 02:03 作者: 女上癮 時間: 2025-3-27 07:57 作者: abreast 時間: 2025-3-27 12:47 作者: zonules 時間: 2025-3-27 15:02
Stefan Edelkamp,Martin Stommeloning strategy to rapidly and efficiently produce recombinant lentiviral vectors for the expression of one or more cDNAs with or without simultaneous shRNAmir expression. Additionally, we describe a luciferase-based approach to rapidly triage shRNAs for knockdown efficacy and specificity without the作者: 粗糙 時間: 2025-3-27 21:39 作者: forager 時間: 2025-3-28 01:46
Zhihong Zhang,Edwin R. Hancock,Xiao Baiods in Molecular Biology.? series format, chapters include introductions to their respective subjects, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and notes sections, highlighting tips on troubleshooting and avoiding known pitfalls...Author作者: osteopath 時間: 2025-3-28 03:42 作者: Muffle 時間: 2025-3-28 07:31 作者: fulcrum 時間: 2025-3-28 12:17 作者: 變化無常 時間: 2025-3-28 16:42 作者: entreat 時間: 2025-3-28 19:48 作者: Apraxia 時間: 2025-3-29 00:51
Smoothing Categorical Dataperiments we show that our approach preserves the large scale structure of a dataset well. That is, the smoothed dataset is simpler while the original and smoothed datasets share the same large scale structure.作者: Left-Atrium 時間: 2025-3-29 05:04 作者: implore 時間: 2025-3-29 07:52
Combining Subjective Probabilities and Data in Training Markov Logic Networkssly used Gaussian priors over weights. We show how one can learn weights in an MLN by combining subjective probabilities and training data, without requiring that the domain expert provides consistent knowledge. Additionally, we also provide a formalism for capturing conditional subjective probabili作者: WITH 時間: 2025-3-29 14:39
Score-Based Bayesian Skill Learningns demonstrate that the new score-based models (a) provide more accurate win/loss probability estimates than TrueSkill when training data is limited, (b) provide competitive and often better win/loss classification performance than TrueSkill, and (c) provide reasonable score outcome predictions with作者: debris 時間: 2025-3-29 18:46
Hypergraph Spectra for Semi-supervised Feature Selectionestablish a novel hypergraph framework which is used for characterizing the multiple relationships within a set of samples. Thus, the structural information latent in the data can be more effectively modeled. Secondly, we derive a hypergraph subspace learning view of feature selection which casting 作者: HEED 時間: 2025-3-29 22:50 作者: 欺騙手段 時間: 2025-3-30 01:58
Machine Learning and Knowledge Discovery in DatabasesEuropean Conference,作者: CURT 時間: 2025-3-30 06:46 作者: 安心地散步 時間: 2025-3-30 11:10 作者: coagulation 時間: 2025-3-30 15:51 作者: 的闡明 時間: 2025-3-30 17:17 作者: agglomerate 時間: 2025-3-31 00:20
Analyzing Text and Social Network Data with Probabilistic Modelsnce, history, medicine, and more. This talk will present an overview of recent work using probabilistic latent variable models to analyze such data. Latent variable models have a long tradition in data analysis and typically hypothesize the existence of simple unobserved phenomena to explain relativ作者: conquer 時間: 2025-3-31 03:03
Discovering Descriptive Tile Trees are difficult to read, or return so many results interpretation becomes impossible. Here, we study a fully automated approach for mining easily interpretable models for binary data. We model data hierarchically with noisy tiles—rectangles with significantly different density than their parent tile.作者: 處理 時間: 2025-3-31 08:53
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performanons. Exact algorithms for these problems exist and are widely used, but their running time is hindered by the need of scanning the entire dataset, possibly multiple times. High quality approximations of FI’s and AR’s are sufficient for most practical uses, and a number of recent works explored the a作者: Brochure 時間: 2025-3-31 11:29
Smoothing Categorical Data one wants to see the large scale structure, one should somehow subtract this smaller scale structure from the model..While for some kinds of model – such as boosted classifiers – it is easy to see the “important” components, for many kind of models this is far harder, if at all possible. In such ca作者: 倔強不能 時間: 2025-3-31 16:34 作者: Cloudburst 時間: 2025-3-31 17:51
Bayesian Network Classifiers with Reduced Precision Parametersaph and a set of conditional probabilities associated with the nodes of the graph. These conditional probabilities are also referred to as parameters of the BNCs. According to common belief, these classifiers are insensitive to deviations of the conditional probabilities under certain conditions. Th作者: organism 時間: 2025-3-31 21:57 作者: 離開可分裂 時間: 2025-4-1 05:46 作者: disciplined 時間: 2025-4-1 08:37
