This paper presents two novel Bayesian learning recovery algorithms for block sparse signals. Two cases are considered. In the first case, the signals within each block are correlated and the block borders are known. ...
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ISBN:
(纸本)9781665482370
This paper presents two novel Bayesian learning recovery algorithms for block sparse signals. Two cases are considered. In the first case, the signals within each block are correlated and the block borders are known. In the second case, the block borders are unknown and the signal elements are uncorrelated. Unlike their existing counterparts, the proposed algorithms obtain the optimal block covariances from the data estimated in the previous iterations. Furthermore, the decision to declare a block as zero is based on hypothesis testing. For the second case, we introduce a new prior model which is characterized by elastic dependencies among neighbouring signal elements. Using this model, we develop a novel Bayesian learning algorithm which iterates between estimating the dependencies among the signal elements and updating the Gaussian prior model. Numerical simulations illustrate the effectiveness of the proposed algorithms.
There has been a steady surge in various sub-fields of machine learning where the focus is on developing systems that learn in an open-ended manner. This is particularly visible in the fields of language grounding and...
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ISBN:
(纸本)9781467366984
There has been a steady surge in various sub-fields of machine learning where the focus is on developing systems that learn in an open-ended manner. This is particularly visible in the fields of language grounding and data stream learning. These systems are designed to evolve as new data arrive, modifying and adjusting learned categories, as well as, accommodating new categories. Although some of the features of incremental learning are present in open-ended learning, the latter can not be characterized as standard incremental learning. This paper presents and discusses the key characteristics of open-ended learning, differentiating it from the standard incremental approaches. The main contribution of this paper is concerned with the evaluation of these algorithms. Typically, the performance of learning algorithms is assessed using traditional train-test methods, such as holdout, cross-validation etc. These evaluation methods are not suited for applications where environments and tasks can change and therefore the learning system is frequently facing new categories. To address this, a well defined and practical protocol is proposed. The utility of the protocol is demonstrated by evaluating and comparing a set of learning algorithms at the task of open-ended visual category learning.
The problem of objective evaluation of learning algorithms is analyzed under the principles of coherence and covariance. The theory of Bayesian information geometry satisfies these principles and encompasses most of t...
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ISBN:
(纸本)0780341236
The problem of objective evaluation of learning algorithms is analyzed under the principles of coherence and covariance. The theory of Bayesian information geometry satisfies these principles and encompasses most of the commonly used learning criteria. Implications to learning theory are discussed.
Cluster-sparse channels often exist in frequency-selective fading broadband communication systems. The main reason is received scattered waveform exhibits cluster structure which is caused by a few reflectors near the...
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ISBN:
(纸本)9789860334074
Cluster-sparse channels often exist in frequency-selective fading broadband communication systems. The main reason is received scattered waveform exhibits cluster structure which is caused by a few reflectors near the receiver. Conventional sparse channel estimation methods have been proposed for general sparse channel model which without considering the potential cluster-sparse structure information. In this paper, we investigate the cluster-sparse channel estimation (CS-CE) problems in the state of the art orthogonal frequency-division multiplexing (OFDM) systems. Novel Bayesian cluster-sparse channel estimation (BCS-CE) methods are proposed to exploit the cluster-sparse structure by using block sparse Bayesian learning (BSBL) algorithm. The proposed methods take advantage of the cluster correlation in training matrix so that they can improve estimation performance. In addition, different from our previous method using uniform block partition information, the proposed methods can work well when the prior block partition information of channels is unknown. Computer simulations show that the proposed method has a superior performance when compared with the previous methods.
Neural networks (NNs) struggle to efficiently solve certain problems, such as learning parities, even when there are simple learning algorithms for those problems. Can NNs discover learning algorithms on their own? We...
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ISBN:
(纸本)9781713871088
Neural networks (NNs) struggle to efficiently solve certain problems, such as learning parities, even when there are simple learning algorithms for those problems. Can NNs discover learning algorithms on their own? We exhibit a NN architecture that, in polynomial time, learns as well as any efficient learning algorithm describable by a constant-sized program. For example, on parity problems, the NN learns as well as Gaussian elimination, an efficient algorithm that can be succinctly described. Our architecture combines both recurrent weight sharing between layers and convolutional weight sharing to reduce the number of parameters down to a constant, even though the network itself may have trillions of nodes. While in practice the constants in our analysis are too large to be directly meaningful, our work suggests that the synergy of Recurrent and Convolutional NNs (RCNNs) may be more natural and powerful than either alone, particularly for concisely parameterizing discrete algorithms.
A Wireless Sensor Network (WSN) is composed of sensor equipped devices that aim at sensing and processing information from the surrounding environment. Energy consumption is the major concern of WSNs. At the same time...
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ISBN:
(纸本)9789812879905;9789812879899
A Wireless Sensor Network (WSN) is composed of sensor equipped devices that aim at sensing and processing information from the surrounding environment. Energy consumption is the major concern of WSNs. At the same time, quality of service is to be considered especially when dealing with critical WSNs. In this paper, we present a game theory based approach to maximize quality of service, defined as the aggregate frame success rate, while optimizing power allocation. Game theory is designed to study interactions between players (e.g. chess players) who decide on a set of actions (e.g. the players moves) to reach the objective outcomes (e.g. to win the game). Here, we model the system as a potential game. We show that the optimal power allocation, crucial in a heterogeneous sensor network, is a Nash equilibrium of this game, and we discuss its uniqueness. For simulations, we present a fully distributed algorithm that drives the whole system to the optimal power allocation.
In this work, we proposed the use of Support Vector Machines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the Meta-Regression appr...
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ISBN:
(纸本)9783540875352
In this work, we proposed the use of Support Vector Machines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the Meta-Regression approach, which has been successfully applied to predict learning performance, supporting algorithm selection. Experiments were performed in a case study in which SVMs with different kernel functions were used to predict the performance of Multi-Layer Perceptron (MLP) networks. The SVMs obtained better results in the evaluated task, when compared to different algorithms that have been applied as meta-regressors in previous work.
作者：
Uehara, KKobe Univ
Res Ctr Urban Safety & Secur Nada Ku Kobe Hyogo 6578501 Japan
In machine learning, it is important to reduce computational time to analyze learning algorithms. Some researchers have attempted to understand learning algorithms by experimenting them on a variety of domains. Others...
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ISBN:
(纸本)3540653902
In machine learning, it is important to reduce computational time to analyze learning algorithms. Some researchers have attempted to understand learning algorithms by experimenting them on a variety of domains. Others have presented theoretical methods of learning algorithm by using approximately mathematical model. The mathematical model has some deficiency that, if the model is too simplified, it may lose the essential behavior of the original algorithm. Furthermore, experimental analyses are based only on informal analyses of the learning task, whereas theoretical analyses address the worst case. Therefore, the results of theoretical analyses are quite different from empirical results. In our framework, called random case analysis, we adopt the idea of randomized algorithms. By using random case analysis, it can predict various aspects of learning algorithm's behavior, and require less computational time than the other theoretical analyses. Furthermore, we can easily apply our framework to practical learning algorithms.
Experimentation of new algorithms is the usual companion section of papers dealing with SAT. However, the behavior of those algorithms is so unpredictable that even strong experiments (hundreds of benchmarks. dozen of...
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ISBN:
(纸本)9783540859574
Experimentation of new algorithms is the usual companion section of papers dealing with SAT. However, the behavior of those algorithms is so unpredictable that even strong experiments (hundreds of benchmarks. dozen of solvers) can be still misleading. We present here a set of experiments of very small changes of a canonical Conflict Driven Clause learning (CDCL) solver and show that even very close versions can lead to very different behaviors. In some cases, the best of them could perfectly have been used to convince the reader of the efficiency of a new method for SAT. This observation can be explained by the lack of real experimental studies of CDCL solvers.
This paper presents an application of learning algorithms to the prediction of HIV-1 phenotypic drug resistance from genotype. The objective of this research consists of two main subjects. The first part is to apply t...
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ISBN:
(纸本)9781424441334
This paper presents an application of learning algorithms to the prediction of HIV-1 phenotypic drug resistance from genotype. The objective of this research consists of two main subjects. The first part is to apply the Support Vector Machine (SVM), the Radial Basis Function Network (the RBF network), and k-Nearest Neighbor (k-NN) to predicting HIV-1 drug resistance. The second part is to study the behavior of each learning algorithms and compare the predictive performance. The results indicate that SVM yields the highest accuracy. The RBF network gives the highest sensitivity whereas k-NN yields the best in specificity.
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