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The use of a full multivariate polynomial model for predictor learning was deemed a daunting task due to its explosive number of expansion terms for high dimensional inputs and high order models. This paper investigates into the viability of using full multivariate polynomials for predictor learning. Particularly, we investigate into the frequently encountered under-determined system with an estimation...
We propose a resource management framework that reduces energy consumption in cloud data centers. The proposed framework predicts the number of virtual machine requests along with their amounts of CPU and memory resources, provides accurate estimations of the number of needed physical machines, and reduces energy consumption by putting to sleep unneeded physical machines. Our framework is based on...
Wireless localization systems have a great importance in a variety of fields such as positioning and tracking systems. Specifically, in hash conditions, e.g., indoor environments, it is difficult to localize an agent with high accuracy due to radio blockage or insufficient information of anchors. Therefore, identification and mitigation of non-line-of-sight (NLOS) radio propagation are highlighted...
The proposed structural fuzzy classification system (SFCS) is an online self-organizing method and automatically identifies the prominent distinct data in the output domain for the new fuzzy rule. Thus, SFCS always tends from higher to lower error region. Both evolving error and rule creation are dynamically realized from the past and current knowledge. Therefore, effective rule-base is the balanced...
Labeling data to train visual concept classifiers requires significant human effort. Active learning addresses labeling overhead by selecting a meaningful subset of data, but often these approaches assume that the set of visual concepts is known in advance. Clustering approaches perform bottom-up discovery of concepts, and reduce labeling effort by moving from instance-based to group-based labeling...
Facial feature detection offers a wide range of applications, e.g. in facial image processing, human computer interaction, consumer electronics, and the entertainment industry. These applications impose two antagonistic key requirements: high processing speed and high detection accuracy. We address both by expanding upon the recently proposed explicit shape regression [1] to (a) allow usage and mixture...
Object information is an important cue to discriminate between activities that draw part of their meaning from context. Most of current work either ignores this information or relies on specific object detectors. However, such object detectors require a significant amount of training data and complicate the transfer of the action recognition framework to novel domains with different objects and object-action...
Combining information from a variety of sources greatly improves the classification accuracy compared with a single source. When the information sources are asynchronous (i.e., the combined feature set has missing values) and training data is limited, the accuracy of existing classification approaches are reduced. In this paper we present CobLE, an approach for creating an ensemble of classifiers...
Highly accurate predictions of load demand and photovoltaic (PV) output have become possible in recent years because of improved measuring instruments and the increase of databases on load demand and PV output. The appropriate control parameters for actual power system operation can be determined by using these predictions. Although parameters determined by conventional methods are accurate, they...
The KDD99 network intrusion contest and the related intrusion data sets attracted increased attention of the research community. The success rate of contest participants was evaluated in terms of the obtained classification cost. The classification cost of the contest winner was 0.2331, the best approach prior to our work carries the classification cost of 0.2224. We show that a simple approach based...
Supervised learning is a commonly used tool for link prediction in social networks, where data imbalance is a major challenge because only a small portion of nodes may have social connections. In this paper, we propose to use a k-nearest neighbor sampling and a random sampling combined approach to address data imbalance issue for social link prediction. In our solution, we use two sampling approaches...
In this paper, we explore the problem of how to learn spectral (e.g., Fourier) models for classification problems. Specifically, we consider two sub-problems of spectral learning: (1) how to select the basis functions that will be included in the model and (2) how to assign coefficients to the selected basis functions. Interestingly, empirical results suggest that the most commonly used approach does...
This paper presents a simple strategy for perception-action of robots in indoor environments using Hierarchical Temporal Memory which is the theory of modeling the rationale of the neocortex. The main idea of the present study is that the input of the HTM network is images of objects that robot perceives in environment, and the output of HTM network is action, such as moving along the wall, moving...
This paper reports and discusses on the initial stage of developing an educational tool to support Chinese undergraduate students in reflecting on their English language (L2) learning experience. By using a classification framework called A-S-E-R and Latent Semantic Analysis, we developed a digital tool to automatically classify reflective L2 learning skills into different elements and hierarchical...
Quality-of-Service (QoS) is a fundamental element in Service-Oriented Computing (SOC) domain. Sufficient and accurate QoS information benefits to the parts not just in-depth analysis in theory but also in system deployments in practice. Studies of predicting missing QoS values is gaining important as an essential step to fast support operations in both academia and industry. Although there are a number...
We consider the problem of classification under the multi-view learning setting referred to as surrogate supervision multi-view learning (SSML). In this setting, training data is provided for two parts of the feature vector (views) in the following format (i) labeled first view examples and (ii) unlabeled first and second view examples. The goal in this setting is to obtain a classifier for the unlabeled...
In multi-instance multi-label (MIML) instance annotation, the goal is to learn an instance classifier while training on a MIML dataset, which consists of bags of instances paired with label sets, instance labels are not provided in the training data. The MIML formulation can be applied in many domains. For example, in an image domain, bags are images, instances are feature vectors representing segments...
Ensembles of classifiers were shown to provide better accuracy than single classifiers. However, the classification robustness is an important performance measure for classifiers and ensembles, besides accuracy, that should be considered. Increasing the robustness of classification systems results in reducing the probability of over-fitting. The robustness, as defined in this study, has not been studied...
Pair wise learning to rank algorithms (such as Rank SVM) teach a machine how to rank objects given a collection of ordered object pairs. However, their accuracy is highly dependent on the abundance of training data. To address this limitation and reduce annotation efforts, the framework of active pair wise learning to rank was introduced recently. However, in such a framework the number of possible...
This paper proposes methods for measuring size and distance of target objects by using mobile devices. For close-range measurement of a size of an object, users must hold the device close to the object and drag it along a desired direction. We develop a new approach for estimating a dragging distance of the device using acceleration signals retrieved from a three-axis accelerometer embedded on it...
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