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- Lijuan Cai, Thomas Hofmann
- CIKM
- 2004

Automatically categorizing documents into pre-defined topic hierarchies or taxonomies is a crucial step in knowledge and content management. Standard machine learning techniques like Support Vector Machines and related large margin methods have been successfully applied for this task, albeit the fact that they ignore the inter-class relationships. In this… (More)

- Lijuan Cai, Thomas Hofmann
- SIGIR
- 2003

Term-based representations of documents have found wide-spread use in information retrieval. However, one of the main shortcomings of such methods is that they largely disregard lexical semantics and, as a consequence, are not sufficiently robust with respect to variations in word usage.In this paper we investigate the use of concept-based document… (More)

- Lijuan Cai, Thomas Hofmann
- IJCAI
- 2007

Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the class relationship between data points. A major disadvantage of LDA is that it fails to discover the local geometrical structure of the data manifold. In this paper, we introduce a novel linear algorithm for discriminant analysis, called Locality Sensitive Discriminant… (More)

- Thomas Hofmann, Lijuan Cai
- 2003

Automatically extracting semantic information about word meaning and document topic from text typically involves an extensive number of classes. Such classes may represent predefined word senses, topics or document categories and are often organized in a taxonomy. The latter encodes important information, which should be exploited in learning classifiers… (More)

- Stuart Andrews, Lijuan Cai, +9 authors Jenine Turner
- 2004

In this paper, we describe how we address the ICML 2004 Physiological Data Modeling Contest. For the gender prediction task, we employ 5 off-the-shelf machine learning methods: decision tree, neural networks, naive bayes, logistic regression, and Support Vector Machines. We use neural networks for the context prediction tasks. Most of the methods perform… (More)

- Lijuan Cai, Wei Ge, Ziqiang Hao
- 2014

According to mathematical properties of continuous function, the universal approximation properties of fuzzy mapping in only one interval are analyzed, and some related theorems are proposed and proved. Then, the sufficient conditions of the general T-S fuzzy systems with universal approximation are derived from these theorems, which helps establish the new… (More)

- T Hofmann, Stuart Andrews, +4 authors Ryan Rifkin
- 2002

Project Title: Support Vector Machines for Multiple Instance Learning PI: T. Hofmann Participants: Stuart Andrews and Thomas Hofmann Abstract: Multiple Instance Learning (MIL) is an important generalization of standard supervised binary classification. In MIL labels are not available for individual training patterns, but are associated with sets of… (More)

- Thomas Hofmann, Lijuan Cai
- 2003

- Lijuan Cai, Zhanhua Cui, Huilin Liu
- 2010 IEEE International Conference on Information…
- 2010

A way to deal with the rule-explosion problem is to use the hierarchical fuzzy systems. In this paper, the hierarchical fuzzy system is constructed first and the property of the hierarchical system is discussed. Then the error properties of the polynomial and hierarchical polynomial are given. Based on these properties, the universal approximation property… (More)

- Wei Ge, Lijuan Cai, Chunling Han
- 2014

Face recognition is a typical problem of pattern recognition and machine learning. Among these approaches to the problem of face recognition, subspace analysis gives the most promising results, and becomes one of the most popular methods. This paper researches typical subspace analysis approaches, based on the introduction of main approaches of linear… (More)