公开(公告)号：US20130097108A1

公开(公告)日：2013-04-18

申请号：US13652087

申请日：2012-10-15

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： Alexandru Niculescu-Mizil ,  Abhishek Kumar ,  Koray Kavukcuoglu

IPC: G06F15/18

CPC classification number: G06N99/005

Abstract: Disclosed are methods and structures of Multiple Kernel learning framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Advantageously, the disclosed methods and structures permit the use of binary classification technologies to develop better performing, and more scalable Multiple Kernel Learning methods that are conceptually simpler.

Abstract translation: 披露的是多核内核学习的方法和结构，被认为是标准的二进制分类问题，其附加约束可以确保学习内核的正确性。 有利地，所公开的方法和结构允许使用二进制分类技术来开发更好的执行和更可扩展的多内核学习方法，其在概念上更简单。

公开(公告)号：US08977579B2

公开(公告)日：2015-03-10

申请号：US13649823

申请日：2012-10-11

Applicant: NEC Laboratories America, Inc.

Inventor： Yunlong He ,  Yanjun Qi ,  Koray Kavukcuoglu

IPC: G06F15/18 ,  G06N99/00 ,  A61M5/00 ,  A61M25/10 ,  A61M25/00

CPC classification number: G06N99/005 ,  A61M5/007 ,  A61M25/0026 ,  A61M25/10 ,  A61M25/1011 ,  A61M2025/105

Abstract: Disclosed is a general learning framework for computer implementation that induces sparsity on the undirected graphical model imposed on the vector of latent factors. A latent factor model SLFA is disclosed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. Advantageously, the model may simultaneously learn the lower-dimensional representation for data and model the pairwise relationships between latent factors explicitly. An on-line learning algorithm is disclosed to make the model amenable to large-scale learning problems. Experimental results on two synthetic data and two real-world data sets demonstrate that pairwise relationships and latent factors learned by the model provide a more structured way of exploring high-dimensional data, and the learned representations achieve the state-of-the-art classification performance.

Abstract translation: 公开了一种用于计算机实现的通用学习框架，其在对潜在因素的向量施加的无向图形模型上引起稀疏性。 潜在因素模型SLFA被公开为具有鼓励协作重建的特殊正则化术语的矩阵分解问题。 有利地，该模型可以同时学习数据的低维表示，并明确地模拟潜在因素之间的成对关系。 公开了一种在线学习算法，使得该模型适合于大规模学习问题。 两个合成数据和两个现实世界数据集的实验结果表明，该模型获得的成对关系和潜在因素提供了一种更结构化的探索高维数据的方法，并且学习的表示实现了最先进的分类 性能。

公开(公告)号：US08838508B2

公开(公告)日：2014-09-16

申请号：US13652087

申请日：2012-10-15

Applicant: NEC Laboratories America, Inc.

Inventor： Alexandru Niculescu-Mizil ,  Abhishek Kumar ,  Koray Kavukcuoglu

IPC: G06F15/18 ,  G06N99/00

CPC classification number: G06N99/005

Abstract: Disclosed are methods and structures of Multiple Kernel learning framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Advantageously, the disclosed methods and structures permit the use of binary classification technologies to develop better performing, and more scalable Multiple Kernel Learning methods that are conceptually simpler.

Abstract translation: 披露的是多核内核学习的方法和结构，被认为是标准的二进制分类问题，其附加约束可以确保学习内核的正确性。 有利地，所公开的方法和结构允许使用二进制分类技术来开发更好的性能和更可扩展的多内核学习方法，其在概念上更简单。