图 书 馆开课单位--滑铁卢大学
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11Asymptotic Theory for Linear-Chain Conditional Random Fields[直链条件随机场渐近理论]
Mathieu Sinn(滑铁卢大学 ) In this theoretical paper we develop an asymptotic theory for Linear-Chain Conditional Random Fields (L-CRFs) and apply it to derive conditions under ...
热度：34
Mathieu Sinn(滑铁卢大学 ) In this theoretical paper we develop an asymptotic theory for Linear-Chain Conditional Random Fields (L-CRFs) and apply it to derive conditions under ...
热度：34
12Theory-Practice Interplay in Machine Learning – Emerging Theoretical Challenges[机器学习中的理论与实践的相互作用 - 新兴的理论挑战]
Shai Ben-David(滑铁卢大学) Theoretical analysis has played a major role in some of the most prominent practical successes of statistical machine learning. However, mainstream ma...
热度：32
Shai Ben-David(滑铁卢大学) Theoretical analysis has played a major role in some of the most prominent practical successes of statistical machine learning. However, mainstream ma...
热度：32
1314Utilizing Unlabeled Data for Classification-Prediction Learning[利用未标记数据进行分类预测学习]
Shai Ben-David(滑铁卢大学) In many classi cation learning tasks, labeled data may be expensive or scarce. At the same time, unlabeled or \weakly labeled" samples, may be av...
热度：51
Shai Ben-David(滑铁卢大学) In many classi cation learning tasks, labeled data may be expensive or scarce. At the same time, unlabeled or \weakly labeled" samples, may be av...
热度：51
15A Domain Adaptation Formal Framework Addressing the Training/Test Distribution Gap[领域适应正式框架解决培训/测试分布差距]
Shai Ben-David(滑铁卢大学) A Domain Adaptation Formal Framework Addressing the Training/Test Distribution Gap
热度：29
Shai Ben-David(滑铁卢大学) A Domain Adaptation Formal Framework Addressing the Training/Test Distribution Gap
热度：29
16A Framework for Probability Density Estimation[概率密度估计框架]
Shai Ben-David(滑铁卢大学) The talk introduces a new framework for learning probability density functions by assessing their performance against a set of 'test functions'. A th...
热度：60
Shai Ben-David(滑铁卢大学) The talk introduces a new framework for learning probability density functions by assessing their performance against a set of 'test functions'. A th...
热度：60
17Subjective Measure for Distribution Similarity[分布相似性的主观测度]
Shai Ben-David(滑铁卢大学) We propose a 'subjective' way of defining similarity between probability distributions.Our measure is parameterized by a collection H of subsets of th...
热度：43
Shai Ben-David(滑铁卢大学) We propose a 'subjective' way of defining similarity between probability distributions.Our measure is parameterized by a collection H of subsets of th...
热度：43
18Discovery of Non-induced Patterns from Sequences[从序列中发现非诱导模式 ]
Andrew K. C. Wong(滑铁卢大学) Discovering patterns from sequence data has significant impact in genomics, proteomics and business. A problem commonly encountered is that the patter...
热度：49
Andrew K. C. Wong(滑铁卢大学) Discovering patterns from sequence data has significant impact in genomics, proteomics and business. A problem commonly encountered is that the patter...
热度：49
19Inductive transfer via embeddings into a common feature space[通过嵌入到公共特征空间的归纳转移]
Shai Ben-David(滑铁卢大学 ) We consider the situation in which there is a basic learning task but different sub-tasks define different data generating distributions. Examples in...
热度：66
Shai Ben-David(滑铁卢大学 ) We consider the situation in which there is a basic learning task but different sub-tasks define different data generating distributions. Examples in...
热度：66
20Characterization of Linkage Based Clustering[基于链接的聚类特征]
David Loker(滑铁卢大学) There are a wide variety of clustering algorithms that, when run on the same data, often produce very different clusterings. Yet there is no principle...
热度：46
David Loker(滑铁卢大学) There are a wide variety of clustering algorithms that, when run on the same data, often produce very different clusterings. Yet there is no principle...
热度：46