Binary Constraint Programming for Big Data and Big Learning


Binary Constraint Programming for Big Data and Big Learning – In this paper, we propose a novel approach to efficient learning of nonlinear models of complex-valued models, which can be used to perform large-scale machine learning tasks. In particular, we first demonstrate the benefits of the new approach when applied to many domains including the task of classification of complex objects. We then use it to improve the generalization performance of our model by training a model which is able to outperform a standard one. The experimental results show that our proposed method, trained on a variety of data sets, has higher accuracy, and has the greatest potential for practical applications.

This paper proposes a method to solve the continuous temporal reasoning question of DPT (discovery and re-iscovery of temporal information). The core assumption underlying the proposed method is that each object is a temporal entity, and its event-related events cannot be represented by any semantic or linguistic properties. We propose the concept of re-orging (orging) temporal entities to model the entity’s event-related events. As long as objects are moving in temporal space, this concept should be sufficient to represent them as temporal entities. The key innovation is the concept of re-orging-ness (the ability to re-org as many objects as it can). We show that, according to the proposed method, all temporal entities in the temporal space can belong to the same entity. To the best of our knowledge, this is the first step toward temporal reasoning in this setting, and we demonstrate that our method performs well in practice and can be applied to any temporal knowledge processing system that is given an input of time series data.

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Binary Constraint Programming for Big Data and Big Learning

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  • Fast k-means using Differentially Private Low-Rank Approximation for Multi-relational Data

    Learning Discrete Event-based Features for Temporal ReasoningThis paper proposes a method to solve the continuous temporal reasoning question of DPT (discovery and re-iscovery of temporal information). The core assumption underlying the proposed method is that each object is a temporal entity, and its event-related events cannot be represented by any semantic or linguistic properties. We propose the concept of re-orging (orging) temporal entities to model the entity’s event-related events. As long as objects are moving in temporal space, this concept should be sufficient to represent them as temporal entities. The key innovation is the concept of re-orging-ness (the ability to re-org as many objects as it can). We show that, according to the proposed method, all temporal entities in the temporal space can belong to the same entity. To the best of our knowledge, this is the first step toward temporal reasoning in this setting, and we demonstrate that our method performs well in practice and can be applied to any temporal knowledge processing system that is given an input of time series data.


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