Radio tomographic imaging (RTI) is an emerging technique which obtains images of passive targets (i.e., not carrying electronic device) within a wireless sensor network using received signal strength (RSS). One major ...
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Radio tomographic imaging (RTI) is an emerging technique which obtains images of passive targets (i.e., not carrying electronic device) within a wireless sensor network using received signal strength (RSS). One major problem that restricts the application of RTI is the difficulty to model the variations of RSS measurements caused by moving targets in different multi-path environments. This paper proposes to apply background learning algorithm to RTI system to model variations. Compared with previous RSS-based device free localization methods, the proposed method achieves higher accuracy in multi-target and time-varying environment without offline training. Firstly, two fundamental background learning algorithms, mixture of gaussians and kernel density estimation, are introduced to calculate the probabilities of links being affected by targets using RSS measurement. Then, Tikhonov regularization is applied to the reconstruction of images using the probabilities. Experimental results show that the proposed approach achieves high accuracy and increases the RSS-network capacity considerably.
Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Meta-learning algorithm presented in this paper is universal and may be applied to any type of CI problems. The nov...
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ISBN:
(纸本)9781424418206
Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Meta-learning algorithm presented in this paper is universal and may be applied to any type of CI problems. The novelty of our proposal lies in complexity controlled testing combined with very useful learning machines generators. The simplest and the best solutions are strongly preferred and are explored earlier. The learning algorithm is augmented by meta-knowledge repository which accumulates information about progress of the search through the space of candidate solutions. The approach facilitates using human experts knowledge to restrict the search space and provide goal definition, gaining meta-knowledge in an automated manner.
In order to rank the performance of machine learning algorithms, many researchers conduct experiments on benchmark data sets. Since most learning algorithms have domain-specific parameters, it is a popular custom to a...
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ISBN:
(纸本)1558604804
In order to rank the performance of machine learning algorithms, many researchers conduct experiments on benchmark data sets. Since most learning algorithms have domain-specific parameters, it is a popular custom to adapt these parameters to obtain a minimal error rate on the test set. The same rate is then used to rank the algorithm, which causes an optimistic bias, We quantify this bias, showing, in particular, that an algorithm with more parameters will probably be ranked higher than an equally good algorithm with fewer parameters. We demonstrate this result, showing the number of parameters and trials required in order to pretend to outperform C4.5 or FOIL, respectively, for various benchmark problems. We then describe out how unbiased ranking experiments should be conducted.
The paper presents the application of signal flow graphs (SFG) and adjoint flow graphs (AFG) in determination the gradient vector for feedforward neural networks. The presented approach is universal and applicable in ...
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ISBN:
(纸本)0818674563
The paper presents the application of signal flow graphs (SFG) and adjoint flow graphs (AFG) in determination the gradient vector for feedforward neural networks. The presented approach is universal and applicable in the same form irrespective of the particular structure of the network. The applicability of the method has been shown on examples of different types of neural networks: multilayer perceptron, sigma-pi network, generalized radial basis network and multilayer Volterra network. The method finds application in any gradient based learning algorithms of neural networks. Some applications of this method, concerning the prediction and identification of the nonlinear dynamic plants are presented and discussed in the paper.
Pairwise learning usually refers to a learning task which involves a loss function depending on pairs of examples, among which most notable ones are bipartite ranking, metric learning and AUC maximization. In this pap...
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Pairwise learning usually refers to a learning task which involves a loss function depending on pairs of examples, among which most notable ones are bipartite ranking, metric learning and AUC maximization. In this paper, we focus on online learning algorithms for pairwise learning problems without strong convexity, for which all previously known algorithms achieve a convergence rate of O(1/root T) after T iterations. In particular, we study an online learning algorithm for pairwise learning with a least-square loss function in an unconstrained setting. We prove that the convergence of its last iterate can converge to the desired minimizer at a rate arbitrarily close to O(1/T) up to logarithmic factor. The rates for this algorithm are established in high probability under the assumptions of polynomially decaying step sizes.
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to test specific properties and failure mo...
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ISBN:
(纸本)9781713829546
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to test specific properties and failure modes of learning algorithms is thus a problem of high interest, as it has a direct impact on innovation in the field. In this sense, we introduce Synbols - Synthetic Symbols - a tool for rapidly generating new datasets with a rich composition of latent features rendered in low resolution images. Synbols leverages the large amount of symbols available in the Unicode standard and the wide range of artistic font provided by the open font community. Our tool's high-level interface provides a language for rapidly generating new distributions on the latent features, including various types of textures and occlusions. To showcase the versatility of Synbols, we use it to dissect the limitations and flaws in standard learning algorithms in various learning setups including supervised learning, active learning, out of distribution generalization, unsupervised representation learning, and object counting.
In recent years a large number of digital mammograms have been generated in hospitals and breast screening centers. To assist diagnosis through indexing those mammogram databases, we proposed a content-based image ret...
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ISBN:
(纸本)3540356258
In recent years a large number of digital mammograms have been generated in hospitals and breast screening centers. To assist diagnosis through indexing those mammogram databases, we proposed a content-based image retrieval framework along with a novel feature extraction technique for describing the degree of calcification phenomenon revealed in the mammograms and six relevance feedback learning algorithms, which fall in the category of query point movement, for improving system performance. The results show that the proposed system can reach a precision rate of 0.716 after five rounds of relevance feedback have been performed.
This paper describes several new online model-free reinforcement learning (RL) algorithms. We designed three new reinforcement algorithms, namely: QV2, QVMAX, and QV-MAX2, that are all based on the QV-learning algorit...
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ISBN:
(纸本)9781424427611
This paper describes several new online model-free reinforcement learning (RL) algorithms. We designed three new reinforcement algorithms, namely: QV2, QVMAX, and QV-MAX2, that are all based on the QV-learning algorithm, but in contrary to QV-learning, QVMAX and QVMAX2 are off-policy RL algorithms and QV2 is a new on-policy RL algorithm. We experimentally compare these algorithms to a large number of different RL algorithms, namely: Q-learning, Sarsa, R-learning, Actor-Critic, QV-learning, and ACLA. We show experiments on five maze problems of varying complexity. Furthermore, we show experimental results on the cart pole balancing problem. The results show that for different problems, there can be large performance differences between the different algorithms, and that there is not a single RL algorithm that always performs best, although on average QV-learning scores highest.
This paper presents both a theoretical discussion and an experimental comparison of batch and incremental learning in an attempt to individuate some of the respective advantages and disadvantages of the two approaches...
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ISBN:
(纸本)3540650687
This paper presents both a theoretical discussion and an experimental comparison of batch and incremental learning in an attempt to individuate some of the respective advantages and disadvantages of the two approaches when learning from frequently updated databases. The paper claims that incremental learning might be more suitable for this purpose, although a number of issues remain to be resolved.
We study the convergence of a class of gradient-based Model-Agnostic Meta-learning (MAML) methods and characterize their overall complexity as well as their best achievable accuracy in terms of gradient norm for nonco...
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We study the convergence of a class of gradient-based Model-Agnostic Meta-learning (MAML) methods and characterize their overall complexity as well as their best achievable accuracy in terms of gradient norm for nonconvex loss functions. We start with the MAML method and its first-order approximation (FO-MAML) and high-light the challenges that emerge in their analysis. By overcoming these challenges not only we provide the first theoretical guarantees for MAML and FO-MAML in nonconvex settings, but also we answer some of the unanswered questions for the implementation of these algorithms including how to choose their learning rate and the batch size for both tasks and datasets corresponding to tasks. In particular, we show that MAML can find an epsilon-first-order stationary point (epsilon-FOSP) for any positive epsilon after at most O(1/epsilon(2)) iterations at the expense of requiring second-order information. We also show that FO-MAML which ignores the second-order information required in the update of MAML cannot achieve any small desired level of accuracy, i.e., FO-MAML cannot find an epsilon-FOSP for any epsilon > 0. We further propose a new-variant of the MAML algorithm called Hessian-free MAML which preserves all theoretical guarantees of MAML, without requiring access to second-order information.
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