开课单位--赫尔辛基大学
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12
Suffix tree and Hidden Markov techniques for pattern analysis[模式分析中的后缀树和Hidden Markov技术]
  Esko Ukkonen(赫尔辛基大学) Suffix tree construction. Mention the new linear time array constructions - - using suffix trees for finding motifs with gaps (some new observations: ...
热度:27

13
Recurrent Predictive Models for Sequence Segmentation[序列分割的递归预测模型]
  Heikki Mannila(赫尔辛基大学) Many sequential data sets have a segmental structure, and similar types of segments occur repeatedly. We consider sequences where the underlying pheno...
热度:31

14
Separating Sources and Analysing Connectivity in EEG/MEG Using Probabilistic Models[基于概率模型的EEG/MEG信号源分离与连通性分析]
  Aapo Hyvärinen(赫尔辛基大学) Currently, there is increasing interest in analysing brain activity in resting state, or under relatively natural conditions such as while watching a ...
热度:45

15
Finding frequent patterns from data[从数据中查找频繁模式]
  Heikki Mannila(赫尔辛基大学) Discovery of frequent patterns = finding positive conjunctions that are true for a given fraction of the observations - this basic idea can be instant...
热度:27

16
The Most Reliable Subgraph Problem[最可靠的子问题]
  Petteri Hintsanen(赫尔辛基大学)
热度:29

17
Biomine search engine for probabilistic graphs[对于概率图Biomine搜索引擎]
  Hannu Toivonen(赫尔辛基大学) Biomine is a search engine prototype under development. It can be used to find biological entities that are (indirectly) related to given query entiti...
热度:23

19
Relevance Feedback Content-Based Image Rertieval with Hierarchical Gaussian Process Bandits[相关反馈的基于图像内容的回收与分层高斯过程土匪]
  Ksenia Konyushkova(赫尔辛基大学) A content-based image retrieval system based on relevance feedback is proposed. The system relies on an interactive search paradigm where at each roun...
热度:34

20
Similarity Analysis by Data Compression[数据压缩的相似性分析]
  Peter Grünwald; Petri Myllymäki(赫尔辛基大学)
热度:25
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