图 书 馆开课单位--约瑟夫·斯特凡学院
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141Cross-lingual named entity disambiguation for concept translation[概念翻译中的跨语言命名实体消歧]
Tadej Štajner(约瑟夫·斯特凡学院) The talk will focus on our experience in developing an integrated natural language processing pipeline, consisting of several distinct components, ope...
热度：101
Tadej Štajner(约瑟夫·斯特凡学院) The talk will focus on our experience in developing an integrated natural language processing pipeline, consisting of several distinct components, ope...
热度：101
142Ontogen Software Demo[Ontogen软件演示]
Blaž Fortuna(约瑟夫·斯特凡学院) We address the problem of constructing light-weight ontology from social network data. As an example we use social network of a mid size research inst...
热度：39
Blaž Fortuna(约瑟夫·斯特凡学院) We address the problem of constructing light-weight ontology from social network data. As an example we use social network of a mid size research inst...
热度：39
143Exploring the space of coding matrix classifiers for hierarchical multiclass text categorization[探索编码的多类文本分类的层次分类矩阵空间]
Janez Brank(约瑟夫·斯特凡学院) One of the ways of approaching a multiclass classification problem is to transform it into several two-class (binary) classification problems. An ense...
热度：36
Janez Brank(约瑟夫·斯特凡学院) One of the ways of approaching a multiclass classification problem is to transform it into several two-class (binary) classification problems. An ense...
热度：36
144145Dealing with structured and unstructured data at Facebook[在Facebook处理结构化和非结构化数据]
Lars Backstrom, Marko Grobelnik(约瑟夫·斯特凡学院) Facebook has undergone tremendous growth in the last five years. Here we will start by looking at some basic statistics and trends that have accompani...
热度：77
Lars Backstrom, Marko Grobelnik(约瑟夫·斯特凡学院) Facebook has undergone tremendous growth in the last five years. Here we will start by looking at some basic statistics and trends that have accompani...
热度：77
146Combining Bagging and Random Subspaces to Create Better Ensembles [将袋装和随机子空间相结合，创造出更好的合奏效果]
Panče Panov(约瑟夫·斯特凡学院) Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples...
热度：47
Panče Panov(约瑟夫·斯特凡学院) Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples...
热度：47
147Sensor Web, Semantic Sensor Networks[语义的传感器网络，传感器网络]
Alexandra Moraru(约瑟夫·斯特凡学院) Sensor Web, Semantic Sensor Networks
热度：10
Alexandra Moraru(约瑟夫·斯特凡学院) Sensor Web, Semantic Sensor Networks
热度：10
148Exposing Real World Information for the Web of Things[揭示物联网的真实世界信息]
Alexandra Moraru(约瑟夫·斯特凡学院) exposing Real World Information for the Web of Things
热度：8
Alexandra Moraru(约瑟夫·斯特凡学院) exposing Real World Information for the Web of Things
热度：8
149Many Faces of Text Processing[文字处理的许多面孔]
Marko Grobelnik(约瑟夫·斯特凡学院) Why people process text with computers? It all started many years ago, with the main goal in minds of researchers, to understand the text. In the mean...
热度：6
Marko Grobelnik(约瑟夫·斯特凡学院) Why people process text with computers? It all started many years ago, with the main goal in minds of researchers, to understand the text. In the mean...
热度：6
150Knowledge technologies for network organisations[网络组织的知识技术]
Mitja Jermol(约瑟夫·斯特凡学院) Knowledge technologies for network organisations
热度：8
Mitja Jermol(约瑟夫·斯特凡学院) Knowledge technologies for network organisations
热度：8