图 书 馆开课单位--康奈尔大学
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61ABC-Boost: Adaptive Base Class Boost for Multi-Class Classification[ABC-Boost：用于多类分类的自适应基类升压]
Ping Li(康奈尔大学) We propose ABC-Boost (Adaptive Base Class Boost) for multi-class classification and present ABC-MART, an implementation of ABC-Boost. The original MA...
热度：229
Ping Li(康奈尔大学) We propose ABC-Boost (Adaptive Base Class Boost) for multi-class classification and present ABC-MART, an implementation of ABC-Boost. The original MA...
热度：229
62Learning Prediction Suffix Trees with Winnow[用Winnow学习预测后缀树]
Nikos Karampatziakis(康奈尔大学) Prediction suffix trees (PSTs) are a popular tool for modeling sequences and have been successfully applied in many domains such as compression and la...
热度：36
Nikos Karampatziakis(康奈尔大学) Prediction suffix trees (PSTs) are a popular tool for modeling sequences and have been successfully applied in many domains such as compression and la...
热度：36
63Fast Solvers and Efficient Implementations for Distance Metric Learning[远程度量学习的快速求解和有效实现]
Kilian Weinberger(康奈尔大学) In this paper we study how to improve nearest neighbor classification by learning a Mahalanobis distance metric. We build on a recently proposed frame...
热度：97
Kilian Weinberger(康奈尔大学) In this paper we study how to improve nearest neighbor classification by learning a Mahalanobis distance metric. We build on a recently proposed frame...
热度：97
64Detecting Statistical Interactions with Additive Groves of Trees[用树的附加树枝检测统计相互作用]
Daria Sorokina(康奈尔大学) Discovering additive structure is an important step towards understanding a complex multi-dimensional function because it allows the function to be ex...
热度：79
Daria Sorokina(康奈尔大学) Discovering additive structure is an important step towards understanding a complex multi-dimensional function because it allows the function to be ex...
热度：79
65Learning Diverse Rankings with Multi-Armed Bandits[学习多种强化学习的不同排名]
Robert Kleinberg(康奈尔大学) Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking f...
热度：78
Robert Kleinberg(康奈尔大学) Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking f...
热度：78
66An Empirical Evaluation of Supervised Learning in High Dimensions[高维监督学习的实证评价]
Nikos Karampatziakis(康奈尔大学) In this paper we perform an empirical evaluation of supervised learning methods on high dimensional data. We evaluate learning performance on three me...
热度：87
Nikos Karampatziakis(康奈尔大学) In this paper we perform an empirical evaluation of supervised learning methods on high dimensional data. We evaluate learning performance on three me...
热度：87
67Training Structural SVMs when Exact Inference is Intractable[当精确推理难以解决时训练结构支持向量机]
Thomas Finley(康奈尔大学) While discriminative training (e.g., CRF, structural SVM) holds much promise for machine translation, image segmentation, and clustering, the complex ...
热度：50
Thomas Finley(康奈尔大学) While discriminative training (e.g., CRF, structural SVM) holds much promise for machine translation, image segmentation, and clustering, the complex ...
热度：50
68Privacy and Background Knowledge[隐私和背景知识]
Johannes Gehrke(康奈尔大学) The digitization of our daily lives has led to an explosion in the collection of data by governments, corporations, and individuals. Protection of con...
热度：42
Johannes Gehrke(康奈尔大学) The digitization of our daily lives has led to an explosion in the collection of data by governments, corporations, and individuals. Protection of con...
热度：42
69Sparse Kernel SVMs via Cutting-Plane Training[通过切割平面训练的核心SVM训练改善稀疏性]
Chun-Nam Yu(康奈尔大学) We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors...
热度：81
Chun-Nam Yu(康奈尔大学) We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors...
热度：81
70Identifying the Original Contribution of a Document via Language Modeling[通过语言建模识别文档的原始思想贡献]
Benyah Shaparenko(康奈尔大学) One major goal of text mining is to provide automatic methods to help humans grasp the key ideas in ever-increasing text corpora. To this effect, we p...
热度：45
Benyah Shaparenko(康奈尔大学) One major goal of text mining is to provide automatic methods to help humans grasp the key ideas in ever-increasing text corpora. To this effect, we p...
热度：45