Case-Based Reasoning provides a framework for integrating domain knowledge with data in the form of four knowledge containers namely Case base, Vocabulary, Similarity and Adaptation. It is a known fact in Case-Based Reasoning community that knowledge can be interchanged between the containers. However, the explicit interplay between them, and how this interchange is affected by the knowledge richness of the underlying domain is not yet fully understood. We attempt to bridge this gap by proposing footprint size reduction as a measure for quantifying knowledge tradeoffs between containers. The proposed measure is empirically evaluated on synthetic as well as real world datasets. From a practical standpoint, footprint size reduction provides a unified way of estimating the impact of a given piece of knowledge in any knowledge container, and can also suggest ways of characterizing the nature of domains ranging from ill-defined to well-defined ones. Our study also makes evident the need for maintenance approaches that go beyond case base and competence to include other containers and performance objectives.

A competence guided casebase maintenance algorithm retains a case in the casebase if it is useful to solve many problems and ensures that the casebase is highly competent. In this paper, we address the compositional adaptation process (of which single case adaptation is a special case) during casebase maintenance by proposing a case competence model for which we propose a measure called retention score to estimate the retention quality of a case. We also propose a revised algorithm based on the retention score to estimate the competent subset of a casebase. We used synthetic datasets to test the effectiveness of the competent subset obtained from the proposed model. We also applied this model in a tutoring application and analyzed the competent subset of concepts in tutoring resources. Empirical results show that the proposed model is effective and overcomes the limitation of footprint-based competence model in compositional adaptation applications.

Towards the goal of enhancing interpretability of Latent Dirichlet Allocation (LDA) topics, we propose WikiLDA, an enhancement to LDA using Wikipedia concepts. In WikiLDA, initially, for each document in a corpus we "sprinkle" (append) its most relevant Wikipedia concepts. We then use Generalized Pólya Urn (GPU) to incorporate word-word, word-concept, and concept-concept semantic relatedness into the generative process of LDA. As the most probable concepts from inferred topics can be referred on Wikipedia, the topics are likely to become more interpretable and hence more usable in acquiring domain knowledge from humans for various text mining tasks (e.g. eliciting topic labels for text classification). Empirical results show that a projection of documents by WikiLDA in a semantically enriched and coherent topic space leads to improved performance in text classification like tasks, especially in domains where the classes are hard to separate.

We report on a novel use of parallel coordinates as a pedagogical tool for illustrating the non-intuitive properties of high dimensional spaces with special emphasis on the phenomenon of Curse of Dimensionality. Also, we have collated what we believe to be a representative sample of diverse approaches that exist in literature to conceptualize the Curse of Dimensionality. We envisage that the paper will have pedagogical value in structuring the way Curse of Dimensionality is presented in classrooms and associated lab sessions.

Conversational recommender systems help users navigate through the product space by exploiting feedback. In conversational systems based on preference-based feedback, the user selects the most preferred item from a list of recommended products. Modelling user's preferences then becomes important in order to recommend relevant items. Several existing recommender systems accomplish this by assuming the features to be independent. Here we will attempt to forego this assumption and exploit the dependencies between the features to build a robust user preference model.

Most approaches for object recognition (OR) use a single feature descriptor to identify the object class from a query image. However, specifically in case of variations in appearance, scale and illumination, the performance of features not only vary depending on the class, but also on the query sample. We propose a biological inspired framework for OR using concepts from feature integration theory (FIT). Our model uses a hierarchy of visual features for OR. The key components in the proposed approach are: (i) SALCUT - unsupervised segmentation for salient object localization; (ii) optimal feature selection - identify appropriate features for each class, at each level of feature hierarchy, for a test instance; (iii) feature combination - which happens at higher levels of feature hierarchy, if features selected at the lower level are unable to classify a test instance. Our method outperforms several state-of-the-art techniques, when validated using two real-world datasets.

Supervised text classifiers require extensive human expertise and labeling efforts. In this paper, we propose a weakly supervised text classification algorithm based on the labeling of Latent Dirichlet Allocation (LDA) topics. Our algorithm is based on the generative property of LDA. In our algorithm, we ask an annotator to assign one or more class labels to each topic, based on its most probable words. We classify a document based on its posterior topic proportions and the class labels of the topics. We also enhance our approach by incorporating domain knowledge in the form of labeled words. We evaluate our approach on four real world text classification datasets. The results show that our approach is more accurate in comparison to semi-supervised techniques from previous work. A central contribution of this work is an approach that delivers effectiveness comparable to the state-of-the-art supervised techniques in hard-to-classify domains, with very low overheads in terms of manual knowledge engineering.

Conversational Recommendation mimics the kind of dialog that takes between a customer and a shopkeeper involving multiple interactions where the user can give feedback at every interaction as opposed to Single Shot Retrieval, which corresponds to a scheme where the system retrieves a set of items in response to a user query in a single interaction. Compromise refers to a particular user preference which the recommender system failed to satisfy. But in the context of conversational systems, where the user's preferences keep on evolving as she interacts with the system, what constitutes as a compromise for her also keeps on changing. Typically, in Single Shot retrieval, the notion of compromise is characterized by the assignment of a particular feature to a particular dominance group such as MIB (higher value is better) or LIB (lower value is better) and this assignment remains true for all the users who use the system.

In this paper, we propose Latent Dirichlet Allocation (LDA) [1] based document classification algorithm which does not require any labeled dataset. In our algorithm, we construct a topic model using LDA, assign one topic to one of the class labels, aggregate all the same class label topics into a single topic using the aggregation property of the Dirichlet distribution and then automatically assign a class label to each unlabeled document depending on its "closeness" to one of the aggregated topics.

In this paper, we look into the problem of filtering problem solution repositories (from sources such as community-driven question answering systems) to render them more suitable for usage in knowledge reuse systems. We explore harnessing the fuzzy nature of usability of a solution to a problem, for such compaction. Fuzzy usabilities lead to several challenges; notably, the trade-off between choosing generic or better solutions. We develop an approach that can heed to a user specification of the trade-off between these criteria and introduce several quality measures based on fuzzy usability estimates to ascertain the quality of a problem-solution repository for usage in a Case Based Reasoning system. We establish, through a detailed empirical analysis, that our approach outperforms state-of-the-art approaches on virtually all quality measures.