Keratocytes are vital for maintaining the overall health of human cornea as they preserve the corneal transparency and help in healing corneal injuries. Manual segmentation of keratocytes is challenging, time consuming and also needs an expert. Here, we propose a novel semi-automatic segmentation framework, called Conditional Random Field Weakly Supervised Segmentation (CRF-WSS) to perform the keratocytes cell segmentation. The proposed framework exploits the concept of dictionary learning in a sparse model along with the Conditional Random Field (CRF) modeling to segment keratocytes cells in Ultra High Resolution Optical Coherence Tomography (UHR-OCT) images of human cornea. The results show higher accuracy for the proposed CRF-WSS framework compare to the other tested Supervised Segmentation (SS) and Weakly Supervised Segmentation (WSS) methods.

Cross-Language Information Retrieval is a compact book introducing a branch of information retrieval that has gained considerable research interest since the dawn of the WorldWideWeb in the mid 1990s. Information retrieval is generally concerned with the problem of finding documents within a large collection that are relevant to a given input query. Whereas the original formulation of IR assumes that queries and documents are written in the same language, cross-language IR (CLIR) presumes instead that they are written in two different languages. If the collection contains documents in more languages, then we refer to multi-lingual IR (MLIR), which is typically solved with multiple instances of CLIR. Recently, other variations on the theme have been proposed that address non-textual documents, such as image, music, and speech retrieval. An interesting application of CLIR is the retrieval of images that are provided with textual descriptions in any language. Computational linguistics could be interested in CLIR for several reasons. CLIR is mainly about the optimal integration ofmachine translation (MT) and IR, and it presents peculiar and difficult translation issues when short queries are involved, which is the most common case. For such problems, interesting approaches have been developed and refined over time, which mainly build on top of core statistical MT techniques (e.g., word alignment models, translation models) and various lexical resources (e.g., WordNet, dictionaries). In recent years, several books on IR have been published (e.g., Grossman and Frieder 2004; Manning, Raghavan, and Schütze 2008; Büttcher, Clarke, and Cormack 2010), which devoted at most a section or chapter to CLIR. As specific books on CLIR have been limited so far to edited collections of scientific papers (Grefenstette 1998), it was definitely time for the first monograph on the topic. Jian-Yun Nie’s volume is structured as five chapters, which are organized as follows:

Concept drift due to hidden changes in context complicates learning in many domains including financial prediction, medical diagnosis, and network performance. Existing machine learning approaches to this problem use an incremental learning, on-line paradigm. Batch, off-line learners tend to be ineffective in domains with hidden changes in context as they assume that the training set is homogeneous. We present an off-line method for identifying hidden context. This method uses an existing batch learner to identify likely context boundaries then performs a form of clustering called contextual clustering. The resulting data sets can then be used to produce context specific, locally stable concepts. The method is evaluated in a simple domain with hidden changes in context.

Concept drift due to hidden changes in context complicates learning in many domains including financial prediction, medical diagnosis, and communication network performance. Existing machine learning approaches to this problem use an incremental learning, on-line paradigm. Batch, off-line learners tend to be ineffective in domains with hidden changes in context as they assume that the training set is homogeneous. An off-line, meta-learning approach for the identification of hidden context is presented. The new approach uses an existing batch learner and the process of contextual clustering to identify stable hidden contexts and the associated context specific, locally stable concepts. The approach is broadly applicable to the extraction of context reflected in time and spatial attributes. Several algorithms for the approach are presented and evaluated. A successful application of the approach to a complex flight simulator control task is also presented.

Concept drift due to hidden changes in context complicates learning in many real world domains including nancial prediction, medical diagnosis, and communication network performance. Machine learning systems addressing this problem generally use an incremental learning, on-line paradigm. An o-line, meta-learning approach to the identication of hidden context is presented. This approach uses an existing batch learner and the process of contextual clustering to identify stable hidden contexts, and the associated, context specic, locally stable concepts. The approach is broadly applicable to a range of domains and learning methods. We describe several evaluation domains and report current progress on these domains.

This paper presents Splice, a batch metalearningsystem, designed to learn locally stableconcepts in domains with hidden changesin context. The majority of machine learningalgorithms assume that target concepts remainstable over time. In many domains thisassumption is invalid. For example, financialprediction, medical diagnosis, and networkperformance are domains in which targetconcepts may not remain stable. Unstabletarget concepts are often due to changesin a hidden context....

This paper investigates the use of strategies to enhance an existing machine learning tool, C4.5, to deal with concept drift and non-determinism in a time series domain. Temporal prediction is a difficult problem faced in most human endeavours. While many specialised time series prediction techniques have been developed, these techniques have limitations. Most are restricted to modeling whole series rather than extracting predictive features and are difficult for domain experts to understand. Symbolic machine learning promises to address these limitations. Symbolic machine learning has been very successful on a broad range of complex problems. To date, few attempts have been made to apply symbolic machine learning directly to temporal prediction. This has resulted in systems that cannot explicitly represent emporally ordered examples or handle changing target concepts. Financial prediction is a challenging target domain, which is temporally ordered, has target concepts that change over time and exhibits a high level of non-determinism. Financial markets are considered to be unpredictable by many academics and thus any improvement on chance is interesting. An aim of this study is to demonstrate that machine learning is capable of providing useful predictive strategies in financial prediction. For short term financial prediction a succesful prediction rate of 60% is considered the minimum useful to domain experts. Our results imply that machine learning can exceed this target with the use of new techniques. By trading off coverage for accuracy, we were able to minimise the effect of both noise and concept drift. The work reported was in collaboration with and funded by Australian Gilt Securities Limited. Michael Harries was supported by an Australian Postgraduate Award (Industrial).