Training linear ranking SVMs in linearithmic time using red-black trees

We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(m*s+m*log(m)) time complexity, where m is the number of training examples, and s the average number of non-zero features per example. Best previously known training algorithms achieve the same efficiency only for restricted special cases, whereas the proposed approach allows any real valued utility scores in the training data. Experiments demonstrate the superior scalability of the proposed approach, when compared to the fastest existing RankSVM implementations.Comment: 20 pages, 4 figure

Learning Output Kernels for Multi-Task Problems

Simultaneously solving multiple related learning tasks is beneficial under a variety of circumstances, but the prior knowledge necessary to correctly model task relationships is rarely available in practice. In this paper, we develop a novel kernel-based multi-task learning technique that automatically reveals structural inter-task relationships. Building over the framework of output kernel learning (OKL), we introduce a method that jointly learns multiple functions and a low-rank multi-task kernel by solving a non-convex regularization problem. Optimization is carried out via a block coordinate descent strategy, where each subproblem is solved using suitable conjugate gradient (CG) type iterative methods for linear operator equations. The effectiveness of the proposed approach is demonstrated on pharmacological and collaborative filtering data

Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data

In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational data, where rankings can be conditioned on unseen data objects. We propose efficient algorithms for conditional ranking by optimizing squared regression and ranking loss functions. We show theoretically, that learning with the ranking loss is likely to generalize better than with the regression loss. Further, we prove that symmetry or reciprocity properties of relations can be efficiently enforced in the learned models. Experiments on synthetic and real-world data illustrate that the proposed methods deliver state-of-the-art performance in terms of predictive power and computational efficiency. Moreover, we also show empirically that incorporating symmetry or reciprocity properties can improve the generalization performance

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Do business games foster skills? A cross-cultural study from learners’ views

Purpose: This study seeks to analyse students’ perception of the effectiveness of business games as an e-learning method in management training. This analysis of games’ effectiveness is centred in the generic and managerial skills acquired, through the comparison of students’ opinions in different cultural contexts within Europe. Design/methodology: The analysis focuses on 120 management students at postgraduate level who use the same business game at different universities in five European countries: Spain, Ireland, Portugal, Italy and Germany. Findings: The results indicate that students positively assessed the generic and specific managerial skills fostered by the business game. The generic skills most valued were information and decision-making, and leadership. Regarding the specific skills, the most valued were management skills and the least valued, skills related to planning and the acquisition of theoretical knowledge. However, significant differences were found between students in different cultural contexts and education systems in the case of certain specific managerial skills. Practical implications: This finding suggests that the students’ perception of how a business game helps them acquire specific managerial skills is influenced by cultural aspects and previous exposure to experiential learning, which determine that the teachers’ role and the teaching process should be adapted to the students’ learning model. Originality/value: With this study, a better knowledge about the students’ perception of this e-learning method is obtained, not just considering a specific educational environment, but comparing opinions of students from different cultural contexts, which adds value to the analyses developed.Peer Reviewe