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Cloud computing has seen rapid growth due to its massive scalability in storage and computing power. Leading the trend, IBM released a hybrid cloud development platform, based on infrastructure as a service. Although tens of thousands of customers visit the platform everyday, a large percentage of trial customers left as their free-trial access expired, and a high proportion of paying customers dropped...
Recommender systems are ubiquitous in applications ranging from e-commerce to social media, helping users to navigate a huge selection of items and to meet a variety of special needs and user tastes. Incorporating contextual knowledge into such systems — such as relational information — has proven to be an effective way to improve recommendation accuracy. A popular line of research aims to model relationships...
Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems due to large search spaces. This paper proposes a three-stage method for multi-domain dialogue policy learning-termed NDQN, and applies it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. In this method, the first stage does multi-policy...
This paper presents an extension of a comparative study of classifier architectures for automatic fault diagnosis, with a special emphasis on the Extreme Learning Machine (ELM), with and without kernel mapping. Besides the explanation of the ELM model, an attempt is made to find theoretical hints of the excellent generalization capabilities of this model, based on the findings of Cover about dichotomies...
Autonomy, adaptability and reactivity are key capabilities of intelligent agents. Many applications of intelligent agents, such as control of ubiquitous computing environments or autonomous robotic systems, demand not only high performance and modeling capability but also the appropriate device or architecture (hardware and related software) for implementing the agent in a real environment. To deal...
Novelty detection is the task of recognising events the differ from a model of normality. This paper proposes an acoustic novelty detector based on neural networks trained with an adversarial training strategy. The proposed approach is composed of a feature extraction stage that calculates Log-Mel spectral features from the input signal. Then, an autoencoder network, trained on a corpus of “normal”...
Unsupervised domain adaptation is an attractive option when labeled data is lacking for some domain of interest but is available for other domain. Part-of-speech (POS) tagging is often considered a solved task when enough labeled data is available in the domain of interest. However, when considering a domain adaptation scenario, this is far from true. Several approaches have been proposed for domain...
This paper presents a robust methodology to find biomarkers that are predictive of any given clinical outcome, by combining three critical steps: Adjustment for correlated biomarkers, through Linkage Disequilibrium pre-processing; False Detection Rate (FD) control with q-values; multivariate predictive modelling with neural networks. The results show that neural network modelling with pre-processing...
Fraud and abuse are two factors directly related to high health care costs, since they correspond to expenses that can be eliminated without prejudice to the quality of services provided. In Brazil, the health insurance companies implement a claim authorization process which assists in the detection of fraud and abuse. This process consists of a prior analysis of the services requested by providers,...
In this paper, we propose an adaptive user distance measurement model to address the challenging problem of modeling user distance from multiple social networks. Previous works construct user distance model in a single social network, and dataset easily leads to over-fitting of the models due to the data sparseness of a single sparse network. We observe that people often simultaneously appear in multiple...
In this paper, we present a spectral clustering method for online and streaming applications. Here, we note that the rank of the coefficients of the eigenvector of the graph Laplacian govern, together with the weights of the adjacency matrix, the assignment of the data to clusters. Thus, we adopt a sampling without replacement strategy, where, at each sampling step, we select those data instances...
Failing to identify multi-word expression (MWE) may cause serious problems for many Natural Language Processing (NLP) tasks. Previous approaches heavily depend on language specific knowledge and pre-existing natural language processing (NLP) tools. However, many languages (including Chinese language) have less such resources and tools compared to English. An automatically learn effective features...
Graph ranking is a promising technique for image retrieval, but its effectiveness is limited by the so-called semantic gap. To mitigate this gap, clickthroughs, which are helpful to perceive the visual content of images, are adopted by graph ranking models recently. However, few existing models take both sparseness and noisiness of clickthroughs into account, which are important in refining the clickthrough-based...
In this paper we propose several novel approaches for incorporating forgetting mechanisms into sequential prediction based machine learning algorithms. The broad premise of our work, supported and motivated in part by recent findings stemming from neurology research on the development of human brains, is that knowledge acquisition and forgetting are complementary processes, and that learning can (perhaps...
This work tackles the seldom discussed task of predicting chaotic time series generated by dynamic systems with evolving parameters. Representative chaotic time series produced by different system dimensions are introduced with a critical parameter linearly depending on time. The evolving character of systems are qualitatively studied by phase portraits. We assess the predictability of different fuzzy...
Forest areas have an important role in the global carbon cycle. They are usually drains of greenhouse gases. From estimates of the soil bulk density Db, it is possible to determine the soil carbon stock. This relation shows the importance of determining Db. The purpose of this paper is to evaluate the performance of pedotransfer functions generated by a Linear Artificial Neural Network (ANN) to estimate...
The main goal of an industrial microgrid during grid-connected operation is maximal cost saving for the microgrid owner. Many industrial companies do not only pay for the amount of electrical energy, but also for the maximum electrical power, which they have drawn from the distribution grid within the billing period. Under these conditions two basic options of cost saving exists utilizing the local...
Neural networks are a powerful function approximation tool which has the ability to model any function with arbitrary precision. For any function as a black box, it is able to reconstruct the function given the target and the input data. However, there are problems where the target is at least partially unknown. In such cases it is impossible for a traditional neural network to compute the gradient...
In this paper, we provide for the first time, error bounds for the off-policy prediction in reinforcement learning. The primary objective in off-policy prediction is to estimate the value function of a given target policy of interest using the linear function approximation architecture by utilizing a sample trajectory generated by a behaviour policy which is possibly different from the target policy...
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