A strategy of maximizing the sum of weighted margins for ranking multi classification problem

This paper discusses the strategies of maximizing the sum of margins for ranking multi classification problem. First, the strategy of maximizing the sum of margins (MSW is extended to maximizing the sum of weighted margins (MSWM). Using MSWM, a mathematical model is established to deal with the ranking multi classification problems where the importance of margins between classes is different, and its dual model is deduced. Then, by introducing the concept of algebraic margin, which is a generalization of geometric margin, the MSWM is further extended to maximizing the sum of weighed algebraic margins (MSWAM). Based on the MSWAM, the deduced mathematical model of the ranking multi classification problem not only has positive generalization ability, but is also a simple linear programming model

Hashing as Tie-Aware Learning to Rank

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive their continuous relaxations, and perform gradient-based optimization with deep neural networks. Our results establish the new state-of-the-art for image retrieval by Hamming ranking in common benchmarks.Comment: 15 pages, 3 figures. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

Trends in Competition and Profitability in the Banking Industry: A Basic Framework

This paper brings to the forefront the assumptions that we make when focusing on a particular type of explanation for bank profitability. We evaluate a broad field of research by introducing a general framework for a profit maximizing bank and demonstrate how different types of models can be fitted into this framework. Next, we present an overview of the current major trends in European banking and relate them to each model’s assumptions, thereby shedding light on the relevance, timeliness and shelf life of the different models. This way, we arrive at a set of recommendations for a future research agenda. We advocate a more prominent role for output prices, and suggest a modification of the intermediation approach. We also suggest ways to more clearly distinguish between market power and efficiency, and explain why we need time-dependent models. Finally, we propose the application of existing models to different size classes and sub-markets. Throughout we emphasize the benefits from applying several, complementary models to overcome the identification problems that we observe in individual models.

Trends in Competition and Profitability in the Banking Industry: A Basic Framework

This paper brings to the forefront the assumptions that we make when focusing on a particular type of explanation for bank profitability. We evaluate a broad field of research by introducing a general framework for a profit maximizing bank and demonstrate how different types of models can be fitted into this framework. Next, we present an overview of the current major trends in European banking and relate them to each model’s assumptions, thereby shedding light on the relevance, timeliness and shelf life of the different models. This way, we arrive at a set of recommendations for a future research agenda. We advocate a more prominent role for output prices, and suggest a modification of the intermediation approach. We also suggest ways to more clearly distinguish between market power and efficiency, and explain why we need time-dependent models. Finally, we propose the application of existing models to different size classes and sub-markets. Throughout we emphasize the benefits from applying several, complementary models to overcome the identification problems that we observe in individual models.

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.Comment: 8 pages, 9 figures, accepted paper for 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, June 201