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ACM Transactions on Intelligent Systems and Technology (TIST), Volume 2 Issue 3, April 2011

Introduction to special issue on machine learning for business applications
Charles X. Ling
Article No.: 18
DOI: 10.1145/1961189.1961190

Prediction in financial markets: The case for small disjuncts
Vasant Dhar
Article No.: 19
DOI: 10.1145/1961189.1961191

Predictive models in regression and classification problems typically have a single model that covers most, if not all, cases in the data. At the opposite end of the spectrum is a collection of models, each of which covers a very small subset of...

A learning-based contrarian trading strategy via a dual-classifier model
Szu-Hao Huang, Shang-Hong Lai, Shih-Hsien Tai
Article No.: 20
DOI: 10.1145/1961189.1961192

Behavioral finance is a relatively new and developing research field which adopts cognitive psychology and emotional bias to explain the inefficient market phenomenon and some irrational trading decisions. Unlike the experts in this field who...

CORN: Correlation-driven nonparametric learning approach for portfolio selection
Bin Li, Steven C.H. Hoi, Vivekanand Gopalkrishnan
Article No.: 21
DOI: 10.1145/1961189.1961193

Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this article, we propose a novel learning-to-trade algorithm termed CORrelation-driven...

Social Network Analysis and Mining for Business Applications
Francesco Bonchi, Carlos Castillo, Aristides Gionis, Alejandro Jaimes
Article No.: 22
DOI: 10.1145/1961189.1961194

Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing...

A helpfulness modeling framework for electronic word-of-mouth on consumer opinion platforms
Richong Zhang, Thomas Tran
Article No.: 23
DOI: 10.1145/1961189.1961195

Electronic Word-of-Mouth (eWOM) is growing exponentially with the rapid development of electronic commerce. As a result, consumers are increasingly crowded by a huge amount of eWOM contents and therefore there is a need to automatically recommend...

Multifocal learning for customer problem analysis
Yong Ge, Hui Xiong, Wenjun Zhou, Siming Li, Ramendra Sahoo
Article No.: 24
DOI: 10.1145/1961189.1961196

In this study, we formalize a multifocal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within each focal group. The multifocal learning problem is motivated by...

Introduction to special issue on large-scale machine learning
Chun-Nan Hsu
Article No.: 25
DOI: 10.1145/1961189.1961197

PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing
Zhiyuan Liu, Yuzhou Zhang, Edward Y. Chang, Maosong Sun
Article No.: 26
DOI: 10.1145/1961189.1961198

Previous methods of distributed Gibbs sampling for LDA run into either memory or communication bottlenecks. To improve scalability, we propose four strategies: data placement, pipeline processing, word bundling, and...

LIBSVM: A library for support vector machines
Chih-Chung Chang, Chih-Jen Lin
Article No.: 27
DOI: 10.1145/1961189.1961199

LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning...

Batch and online learning algorithms for nonconvex neyman-pearson classification
Gilles Gasso, Aristidis Pappaioannou, Marina Spivak, Léon Bottou
Article No.: 28
DOI: 10.1145/1961189.1961200

We describe and evaluate two algorithms for Neyman-Pearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint...

Learning to recommend with explicit and implicit social relations
Hao Ma, Irwin King, Michael R. Lyu
Article No.: 29
DOI: 10.1145/1961189.1961201

Recommender systems have been well studied and developed, both in academia and in industry recently. However, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the...

Learning to detect malicious URLs
Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker
Article No.: 30
DOI: 10.1145/1961189.1961202

Malicious Web sites are a cornerstone of Internet criminal activities. The dangers of these sites have created a demand for safeguards that protect end-users from visiting them. This article explores how to detect malicious Web sites from the...