Abstra t. Most evaluation metri s in lassi ation are designed to reward lass uniformity in the example subsets indu ed by a feature (e.g., Information Gain). Other metri s are designed to reward dis rimination power in the ontext of feature sele tion as a means to ombat the feature-intera tion problem (e.g., Relief, Contextual Merit). We de ne a new framework that ombines the strengths of both kinds of metri s. Our framework enri hes the available information when onsidering whi h feature to use to partition the training set. Sin e most metri s rely on only a small fra tion of this information, this framework enlarges the spa e of possible metri s. Experiments on real-world domains in the ontext of de ision-tree learning show how a simple setting for our framework ompares well with standard metri s.

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: