National Conference on Artificial Intelligence, 2006
We present a novel framework for multi-label learning that explicitly addresses the challenge ari... more We present a novel framework for multi-label learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data.
National Conference on Artificial Intelligence, 2006
Learning application-specific distance metrics from labeled data is critical for both statistical... more Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area has focused on finding metrics that simultaneously optimize compactness and separability in a global sense. Specifically, such distance metrics attempt to keep all of the data points in each class close together while ensuring that data points from different classes are separated. However, particularly when classes exhibit multimodal data distributions, these goals conflict and thus cannot be simultaneously satisfied. This paper proposes a Local Distance Metric (LDM) that aims to optimize local compactness and local separability. We present an efficient algorithm that employs eigenvector analysis and bound optimization to learn the LDM from training data in a probabilistic framework. We demonstrate that LDM achieves significant improvements in both classification and retrieval accuracy compared to global distance learning and kernel-based KNN.
Many machine learning algorithms, such as K Nearest Neighbor (KNN), heavily rely on the distance ... more Many machine learning algorithms, such as K Nearest Neighbor (KNN), heavily rely on the distance metric for the input data patterns. Distance Metric learning is to learn a distance metric for the input space of data from a given collection of pair of similar/dissimilar points that preserves the distance relation among the training data. In recent years, many studies have demonstrated, both empirically and theoretically, that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. This paper surveys the field of distance metric learning from a principle perspective, and includes a broad selection of recent work. In particular, distance metric learning is reviewed under different learning conditions: supervised learning versus unsupervised learning, learning in a global sense versus in a local sense; and the distance matrix based on linear kernel versus nonlinear kernel. In addition, this paper discusses a number of techniques that is central to distance metric learning, including convex programming, positive semi-definite programming, kernel learning, dimension reduction, K Nearest Neighbor, large margin classification, and graph-based approaches.
Podophyllotoxin is a naturally occurring lignan with important antineoplastic and antiviral prope... more Podophyllotoxin is a naturally occurring lignan with important antineoplastic and antiviral properties and supported by detailed understanding of their mechanism of action, and facilitated by chemical manipulations that have amplified their bioactivity, the podophyllotoxin analogues have advanced to the forefront of several areas of therapeutic and developmental chemotherapy. Additive and synergistic laboratory interactions with other cytotoxic drugs have been exploited to allow development of podophyllotoxin-based multidrug regimens, which are showing important activity in several malignancies, and many of its related analogues will complement conventional pharmaceuticals in treatment, prevention and diaganosis of disease, while at the same time adding value to agriculture. Additive and synergistic laboratory interactions with other cytotoxic drugs have been exploited to allow development of etoposide-based multidrug regimens, which are showing important activity in several malignancies. Extensive structural modifications of podophyllotoxin have been performed in order to obtain more potent and less toxic antitumour agents, which resulted in the widespread clinical introduction of two semisynthetic glucoconjugate analogues of etoposide and teniposide and newer agents with promising preclinical activity are in various stages of clinical assessment. As knowledge of molecular and biochemical mechanisms of action and resistance continues to expand, newer and better podophyllotoxin-based strategies for treatment of malignant disease are likely to evolve. This review provides a detailed discussion of research advances in the synthetic and medicinal chemistry of podophyllotoxin, and addresses the short history and pharmacological action of these compounds and further outlines the preclinical development and clinical trials of drugs in the pipeline and marketing approval. Finally, a systemic evaluation of novel and important analogues of podophyllotoxin and their contribution to the current structure-activity profile are considered. It is hoped that this review will be able to address the contributions of podophyllotoxin-related research to overall drug discovery and development and the role that this field will play in future.
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Papers by Liu Yang