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Dataset Shift in Machine Learning
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail...
Quantitative Methods in Cognitive Semantics
Review text: "Overall, this volume is an important contribution to the development of empirical Cognitive Semantics. This collection of high-quality papers provides the reader with an insight into the most important empirical approaches in corpus-driven semantic research."Natalia Levshina in: Linguist List 20.3011.
Formal and Methodological Approaches to Applied Linguistics
The goal of this Special Issue is to bring together state-of-the art articles on applied linguistics which reflect investigation carried out by researchers from different parts of the world. By bringing together papers from different perspectives, we hope to be able to gain a better understanding of the field. Hence, this Special Issue intends to address the study of language in its different dimensions and within the framework of multiple methodologies and formal accounts as used by researchers in the field. This Special Issue is dedicated to research in any area related to applied linguistics, including language acquisition and language learning; language teaching and curriculum design; language for specific purposes; psychology of language, child language and psycholinguistics; sociolinguistics; pragmatics; discourse analysis; corpus linguistics, computational linguistics and language engineering; lexicology and lexicography; and translation and interpretation.
Selecting Models from Data
This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.
Target Discovery and Validation Reviews and Protocols
Target discovery is a field that has existed for several years but is so vibrant today because of the recent progress in our understanding of the molecular mechanisms of many human diseases and the technical advances in target identification and validation. More sophisticated gene profiling technologies, such as DNA microarrays and serial analysis of gene expression, permit rapid identification of lead targets. Moreover, analysis of gene networks in living organisms allows the identification of target genes that operate in defined physiological pathways. With the sequencing of several genomes completed and the rapidly growing gene expression databases, there is now greater impetus than ever ...
Statistical Methods in Molecular Evolution
In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory sect...
Grid Computing in Life Science
Researchers in the ?eld of life sciences rely increasingly on information te- nology to extract and manage relevant knowledge. The complex computational and data management needs of life science research make Grid technologies an attractive support solution. However, many important issues must be addressed before the Life Science Grid becomes commonplace. The 1st International Life Science Grid Workshop (LSGRID 2004) was held in Kanazawa Japan, May 31–June 1, 2004. This workshop focused on life s- ence applications of grid systems especially for bionetwork research and systems biology which require heterogeneous data integration from genome to phenome, mathematical modeling and simulation ...
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Lifelong Machine Learning
Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requir...
