Attribute Exploration of Discrete Temporal Transitions

Discrete temporal transitions occur in a variety of domains, but this work is mainly motivated by applications in molecular biology: explaining and analyzing observed transcriptome and proteome time series by literature and database knowledge. The starting point of a formal concept analysis model is presented. The objects of a formal context are states of the interesting entities, and the attributes are the variable properties defining the current state (e.g. observed presence or absence of proteins). Temporal transitions assign a relation to the objects, defined by deterministic or non-deterministic transition rules between sets of pre- and postconditions. This relation can be generalized to its transitive closure, i.e. states are related if one results from the other by a transition sequence of arbitrary length. The focus of the work is the adaptation of the attribute exploration algorithm to such a relational context, so that questions concerning temporal dependencies can be asked during the exploration process and be answered from the computed stem base. Results are given for the abstract example of a game and a small gene regulatory network relevant to a biomedical question.Comment: Only the email address and reference have been replace

Speech-plans: Generating evaluative responses in spoken dialogue

Recent work on evaluation of spoken dialogue systems indicates that better algorithms are needed for the presentation of complex information in speech. Current dialogue systems often rely on presenting sets of options and their attributes sequentially. This places a large memory burden on users, who have to remember complex trade-offs between multiple options and their attributes. To address these problems we build on previous work using multiattribute decision theory to devise speech-planning algorithms that present usertailored summaries, comparisons and recommendations that allow users to focus on critical differences between options and their attributes. We discuss the differences between speech and text planning that result from the particular demands of the speech situation.

When retailing and Las Vegas meet: probabilistic free price promotions

A number of retailers offer gambling- or lottery-type price promotions with a chance to receive one’s entire purchase for free. Although these retailers seem to share the intuition that probabilistic free price promotions are attractive to consumers, it is unclear how they compare to traditional sure price promotions of equal expected monetary value. We compared these two risky and sure price promotions for planned purchases across six experiments in the field and in the laboratory. Together, we found that consumers are not only more likely to purchase a product promoted with a probabilistic free discount over the same product promoted with a sure discount but that they are also likely to purchase more of it. This preference seems to be primarily due to a diminishing sensitivity to the prices. In addition, we find that the zero price effect, transaction cost, and novelty considerations are likely not implicated.https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2015.2328Published versio

Introduction in IND and recursive partitioning

This manual describes the IND package for learning tree classifiers from data. The package is an integrated C and C shell re-implementation of tree learning routines such as CART, C4, and various MDL and Bayesian variations. The package includes routines for experiment control, interactive operation, and analysis of tree building. The manual introduces the system and its many options, gives a basic review of tree learning, contains a guide to the literature and a glossary, and lists the manual pages for the routines and instructions on installation

Intention superiority as a mechanism of the question-behavior effect

This paper investigates the mere measurement effect from an intention superiority perspective. Relying on the dynamic processes that characterize intention-related information in memory, the first study shows that a brand tied to an intention remains in a heightened state of activation until choice, after which it becomes inhibited. Competitive brands that are distracting from intention completion are inhibited prior to the completion of the intention. These changes in brand activation drive the mere measurement effect. Two additional studies show that intention superiority can explain findings that cannot be accounted for by traditional theoretical explanations, such as increased choice of the preferred brand after activation of a negatively evaluated brand and decreased choice of the preferred brand when consumers make two subsequent choices

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view Learning, providing in particular a unifying framework linking these two important learning approaches. In the case of the least square loss function, we provide a closed form solution, which is obtained by solving a system of linear equations. In the case of Support Vector Machine (SVM) classification, our formulation generalizes in particular both the binary Laplacian SVM to the multi-class, multi-view settings and the multi-class Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is obtained by solving a single quadratic optimization problem, as in standard SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results obtained on the task of object recognition, using several challenging datasets, demonstrate the competitiveness of our algorithms compared with other state-of-the-art methods.Comment: 72 page