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Journal articles on the topic 'Unsupervised neural networks'

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Published: 4 June 2021
Last updated: 19 February 2022

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1

Murnion, Shane D. "Spatial analysis using unsupervised neural networks." Computers & Geosciences 22, no. 9 (November 1996): 1027–31. http://dx.doi.org/10.1016/s0098-3004(96)00041-6.

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2

Luo, Shuyue, Shangbo Zhou, Yong Feng, and Jiangan Xie. "Pansharpening via Unsupervised Convolutional Neural Networks." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 4295–310. http://dx.doi.org/10.1109/jstars.2020.3008047.

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3

Meuleman, J., and C. van Kaam. "UNSUPERVISED IMAGE SEGMENTATION WITH NEURAL NETWORKS." Acta Horticulturae, no. 562 (November 2001): 101–8. http://dx.doi.org/10.17660/actahortic.2001.562.10.

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4

Gunhan, Atilla E., László P. Csernai, and Jørgen Randrup. "UNSUPERVISED COMPETITIVE LEARNING IN NEURAL NETWORKS." International Journal of Neural Systems 01, no. 02 (January 1989): 177–86. http://dx.doi.org/10.1142/s0129065789000086.

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We study an idealized neural network that may approximate certain neurophysiological features of natural neural systems. The network contains a mutual lateral inhibition and is subjected to unsupervised learning by means of a Hebb-type learning principle. Its learning ability is analysed as a function of the strength of lateral inhibition and the training set.
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5

Becker, Suzanna. "UNSUPERVISED LEARNING PROCEDURES FOR NEURAL NETWORKS." International Journal of Neural Systems 02, no. 01n02 (January 1991): 17–33. http://dx.doi.org/10.1142/s0129065791000030.

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Supervised learning procedures for neural networks have recently met with considerable success in learning difficult mappings. However, their range of applicability is limited by their poor scaling behavior, lack of biological plausibility, and restriction to problems for which an external teacher is available. A promising alternative is to develop unsupervised learning algorithms which can adaptively learn to encode the statistical regularities of the input patterns, without being told explicitly the correct response for each pattern. In this paper, we describe the major approaches that have been taken to model unsupervised learning, and give an in-depth review of several examples of each approach.
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6

Hamad, D., C. Firmin, and J. G. Postaire. "Unsupervised pattern classification by neural networks." Mathematics and Computers in Simulation 41, no. 1-2 (June 1996): 109–16. http://dx.doi.org/10.1016/0378-4754(95)00063-1.

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7

Vamaraju, Janaki, and Mrinal K. Sen. "Unsupervised physics-based neural networks for seismic migration." Interpretation 7, no. 3 (August 1, 2019): SE189—SE200. http://dx.doi.org/10.1190/int-2018-0230.1.

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We have developed a novel framework for combining physics-based forward models and neural networks to advance seismic processing and inversion algorithms. Migration is an effective tool in seismic data processing and imaging. Over the years, the scope of these algorithms has broadened; today, migration is a central step in the seismic data processing workflow. However, no single migration technique is suitable for all kinds of data and all styles of acquisition. There is always a compromise on the accuracy, cost, and flexibility of these algorithms. On the other hand, machine-learning algorithms and artificial intelligence methods have been found immensely successful in applications in which big data are available. The applicability of these algorithms is being extensively investigated in scientific disciplines such as exploration geophysics with the goal of reducing exploration and development costs. In this context, we have used a special kind of unsupervised recurrent neural network and its variants, Hopfield neural networks and the Boltzmann machine, to solve the problems of Kirchhoff and reverse time migrations. We use the network to migrate seismic data in a least-squares sense using simulated annealing to globally optimize the cost function of the neural network. The weights and biases of the neural network are derived from the physics-based forward models that are used to generate seismic data. The optimal configuration of the neural network after training corresponds to the minimum energy of the network and thus gives the reflectivity solution of the migration problem. Using synthetic examples, we determine that (1) Hopfield neural networks are fast and efficient and (2) they provide reflectivity images with mitigated migration artifacts and improved spatial resolution. Specifically, the presented approach minimizes the artifacts that arise from limited aperture, low subsurface illumination, coarse sampling, and gaps in the data.
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8

Xu, Jianqiao, Zhaolu Zuo, Danchao Wu, Bing Li, Xiaoni Li, and Deyi Kong. "Bearing Defect Detection with Unsupervised Neural Networks." Shock and Vibration 2021 (August 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/9544809.

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Bearings always suffer from surface defects, such as scratches, black spots, and pits. Those surface defects have great effects on the quality and service life of bearings. Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. However, it is difficult to realize automatic detection of bearing surface defects based on data-driven-based deep learning due to few samples data of bearing defects on the actual production line. Sample preprocessing algorithm based on normalized sample symmetry of bearing is adopted to greatly increase the number of samples. Two different convolutional neural networks, supervised networks and unsupervised networks, are tested separately for the bearing defect detection. The first experiment adopts the supervised networks, and ResNet neural networks are selected as the supervised networks in this experiment. The experiment result shows that the AUC of the model is 0.8567, which is low for the actual use. Also, the positive and negative samples should be labelled manually. To improve the AUC of the model and the flexibility of the samples labelling, a new unsupervised neural network based on autoencoder networks is proposed. Gradients of the unlabeled data are used as labels, and autoencoder networks are created with U-net to predict the output. In the second experiment, positive samples of the supervised experiment are used as the training set. The experiment of the unsupervised neural networks shows that the AUC of the model is 0.9721. In this experiment, the AUC is higher than the first experiment, but the positive samples must be selected. To overcome this shortage, the dataset of the third experiment is the same as the supervised experiment, where all the positive and negative samples are mixed together, which means that there is no need to label the samples. This experiment shows that the AUC of the model is 0.9623. Although the AUC is slightly lower than that of the second experiment, the AUC is high enough for actual use. The experiment results demonstrate the feasibility and superiority of the proposed unsupervised networks.
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9

Raja, Muhammad Asif Zahoor. "Unsupervised neural networks for solving Troesch's problem." Chinese Physics B 23, no. 1 (January 2014): 018903. http://dx.doi.org/10.1088/1674-1056/23/1/018903.

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10

Parisi, Daniel R., Marı́a C. Mariani, and Miguel A. Laborde. "Solving differential equations with unsupervised neural networks." Chemical Engineering and Processing: Process Intensification 42, no. 8-9 (August 2003): 715–21. http://dx.doi.org/10.1016/s0255-2701(02)00207-6.

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11

Ergen, Tolga, and Suleyman Serdar Kozat. "Unsupervised Anomaly Detection With LSTM Neural Networks." IEEE Transactions on Neural Networks and Learning Systems 31, no. 8 (August 2020): 3127–41. http://dx.doi.org/10.1109/tnnls.2019.2935975.

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12

Jonker, H. J. J., and A. C. C. Coolen. "Unsupervised dynamic learning in layered neural networks." Journal of Physics A: Mathematical and General 24, no. 17 (September 7, 1991): 4219–34. http://dx.doi.org/10.1088/0305-4470/24/17/032.

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13

Zhou, Errui, Liang Fang, and Binbin Yang. "Memristive Spiking Neural Networks Trained with Unsupervised STDP." Electronics 7, no. 12 (December 6, 2018): 396. http://dx.doi.org/10.3390/electronics7120396.

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Neuromorphic computing systems are promising alternatives in the fields of pattern recognition, image processing, etc. especially when conventional von Neumann architectures face several bottlenecks. Memristors play vital roles in neuromorphic computing systems and are usually used as synaptic devices. Memristive spiking neural networks (MSNNs) are considered to be more efficient and biologically plausible than other systems due to their spike-based working mechanism. In contrast to previous SNNs with complex architectures, we propose a hardware-friendly architecture and an unsupervised spike-timing dependent plasticity (STDP) learning method for MSNNs in this paper. The architecture, which is friendly to hardware implementation, includes an input layer, a feature learning layer and a voting circuit. To reduce hardware complexity, some constraints are enforced: the proposed architecture has no lateral inhibition and is purely feedforward; it uses the voting circuit as a classifier and does not use additional classifiers; all neurons can generate at most one spike and do not need to consider firing rates and refractory periods; all neurons have the same fixed threshold voltage for classification. The presented unsupervised STDP learning method is time-dependent and uses no homeostatic mechanism. The MNIST dataset is used to demonstrate our proposed architecture and learning method. Simulation results show that our proposed architecture with the learning method achieves a classification accuracy of 94.6%, which outperforms other unsupervised SNNs that use time-based encoding schemes.
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14

Resta, Marina, Michele Sonnessa, Elena Tànfani, and Angela Testi. "Unsupervised neural networks for clustering emergent patient flows." Operations Research for Health Care 18 (September 2018): 41–51. http://dx.doi.org/10.1016/j.orhc.2017.08.002.

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15

Ryu, Jongbin, Ming-Hsuan Yang, and Jongwoo Lim. "Unsupervised feature learning for self-tuning neural networks." Neural Networks 133 (January 2021): 103–11. http://dx.doi.org/10.1016/j.neunet.2020.10.011.

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16

Hinton, G., P. Dayan, B. Frey, and R. Neal. "The "wake-sleep" algorithm for unsupervised neural networks." Science 268, no. 5214 (May 26, 1995): 1158–61. http://dx.doi.org/10.1126/science.7761831.

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17

Begin, J., and R. Proulx. "Categorization in unsupervised neural networks: the Eidos model." IEEE Transactions on Neural Networks 7, no. 1 (January 1996): 147–54. http://dx.doi.org/10.1109/72.478399.

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18

Elazouni, Ashraf M. "Classifying Construction Contractors Using Unsupervised-Learning Neural Networks." Journal of Construction Engineering and Management 132, no. 12 (December 2006): 1242–53. http://dx.doi.org/10.1061/(asce)0733-9364(2006)132:12(1242).

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19

MUHAMMED, HAMED HAMID. "UNSUPERVISED FUZZY CLUSTERING USING WEIGHTED INCREMENTAL NEURAL NETWORKS." International Journal of Neural Systems 14, no. 06 (December 2004): 355–71. http://dx.doi.org/10.1142/s0129065704002121.

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A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. Only two parameters must be chosen by the user for the FC-WINN algorithm to determine the resolution and the connectedness of the net. Other parameters that must be specified are those which are necessary for the used incremental neural network, which is a modified version of the Growing Neural Gas algorithm (GNG). The FC-WINN algorithm is computationally efficient when compared to other approaches for clustering large high-dimensional data sets.
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20

Atiya, Amir F. "An unsupervised learning technique for artificial neural networks." Neural Networks 3, no. 6 (January 1990): 707–11. http://dx.doi.org/10.1016/0893-6080(90)90058-s.

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21

Pavlidis, N. G., V. P. Plagianakos, D. K. Tasoulis, and M. N. Vrahatis. "Financial forecasting through unsupervised clustering and neural networks." Operational Research 6, no. 2 (May 2006): 103–27. http://dx.doi.org/10.1007/bf02941227.

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22

Bernert, Marie, and Blaise Yvert. "An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting." International Journal of Neural Systems 29, no. 08 (September 25, 2019): 1850059. http://dx.doi.org/10.1142/s0129065718500594.

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Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal in an online and unsupervised fashion. The signal stream is continuously fed to the network and processed through several layers to output spike trains matching the truth after a short learning period requiring only few data. The network features an attention mechanism to handle the scarcity of action potential occurrences in the signal, and a threshold adaptation mechanism to handle patterns with different sizes. This method outperforms two existing spike-sorting algorithms at low signal-to-noise ratio (SNR) and can be adapted to process several channels simultaneously in the case of tetrode recordings. Such attention-based STDP network applied to spike-sorting opens perspectives to embed neuromorphic processing of neural data in future brain implants.
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23

Krotov, Dmitry, and John J. Hopfield. "Unsupervised learning by competing hidden units." Proceedings of the National Academy of Sciences 116, no. 16 (March 29, 2019): 7723–31. http://dx.doi.org/10.1073/pnas.1820458116.

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It is widely believed that end-to-end training with the backpropagation algorithm is essential for learning good feature detectors in early layers of artificial neural networks, so that these detectors are useful for the task performed by the higher layers of that neural network. At the same time, the traditional form of backpropagation is biologically implausible. In the present paper we propose an unusual learning rule, which has a degree of biological plausibility and which is motivated by Hebb’s idea that change of the synapse strength should be local—i.e., should depend only on the activities of the pre- and postsynaptic neurons. We design a learning algorithm that utilizes global inhibition in the hidden layer and is capable of learning early feature detectors in a completely unsupervised way. These learned lower-layer feature detectors can be used to train higher-layer weights in a usual supervised way so that the performance of the full network is comparable to the performance of standard feedforward networks trained end-to-end with a backpropagation algorithm on simple tasks.
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24

Aragon-Calvo, M. A., and J. C. Carvajal. "Self-supervised learning with physics-aware neural networks – I. Galaxy model fitting." Monthly Notices of the Royal Astronomical Society 498, no. 3 (September 7, 2020): 3713–19. http://dx.doi.org/10.1093/mnras/staa2228.

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ABSTRACT Estimating the parameters of a model describing a set of observations using a neural network is, in general, solved in a supervised way. In cases when we do not have access to the model’s true parameters, this approach can not be applied. Standard unsupervised learning techniques, on the other hand, do not produce meaningful or semantic representations that can be associated with the model’s parameters. Here we introduce a novel self-supervised hybrid network architecture that combines traditional neural network elements with analytic or numerical models, which represent a physical process to be learned by the system. Self-supervised learning is achieved by generating an internal representation equivalent to the parameters of the physical model. This semantic representation is used to evaluate the model and compare it to the input data during training. The semantic autoencoder architecture described here shares the robustness of neural networks while including an explicit model of the data, learns in an unsupervised way, and estimates, by construction, parameters with direct physical interpretation. As an illustrative application, we perform unsupervised learning for 2D model fitting of exponential light profiles and evaluate the performance of the network as a function of network size and noise.
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25

Zhang, Pengfei, and Xiaoming Ju. "Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks." Mathematical Problems in Engineering 2021 (September 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/8268249.

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It is important to detect adversarial samples in the physical world that are far away from the training data distribution. Some adversarial samples can make a machine learning model generate a highly overconfident distribution in the testing stage. Thus, we proposed a mechanism for detecting adversarial samples based on semisupervised generative adversarial networks (GANs) with an encoder-decoder structure; this mechanism can be applied to any pretrained neural network without changing the network’s structure. The semisupervised GANs also give us insight into the behavior of adversarial samples and their flow through the layers of a deep neural network. In the supervised scenario, the latent feature of the semisupervised GAN and the target network’s logit information are used as the input of the external classifier support vector machine to detect the adversarial samples. In the unsupervised scenario, first, we proposed a one-class classier based on the semisupervised Gaussian mixture conditional generative adversarial network (GM-CGAN) to fit the joint feature information of the normal data, and then, we used a discriminator network to detect normal data and adversarial samples. In both supervised scenarios and unsupervised scenarios, experimental results show that our method outperforms latest methods.
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26

Lo, James Ting-Ho. "A Low-Order Model of Biological Neural Networks." Neural Computation 23, no. 10 (October 2011): 2626–82. http://dx.doi.org/10.1162/neco_a_00166.

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A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.
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27

Chakravarty, Aniv, and Jagadish S. Kallimani. "Unsupervised Multi-Document Abstractive Summarization Using Recursive Neural Network with Attention Mechanism." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3867–72. http://dx.doi.org/10.1166/jctn.2020.8976.

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Text summarization is an active field of research with a goal to provide short and meaningful gists from large amount of text documents. Extractive text summarization methods have been extensively studied where text is extracted from the documents to build summaries. There are various type of multi document ranging from different formats to domains and topics. With the recent advancement in technology and use of neural networks for text generation, interest for research in abstractive text summarization has increased significantly. The use of graph based methods which handle semantic information has shown significant results. When given a set of documents of English text files, we make use of abstractive method and predicate argument structures to retrieve necessary text information and pass it through a neural network for text generation. Recurrent neural networks are a subtype of recursive neural networks which try to predict the next sequence based on the current state and considering the information from previous states. The use of neural networks allows generation of summaries for long text sentences as well. This paper implements a semantic based filtering approach using a similarity matrix while keeping all stop-words. The similarity is calculated using semantic concepts and Jiang–Conrath similarity and making use of a recurrent neural network with an attention mechanism to generate summary. ROUGE score is used for measuring accuracy, precision and recall scores.
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28

Browne, David, Michael Giering, and Steven Prestwich. "PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing." Remote Sensing 12, no. 7 (March 29, 2020): 1092. http://dx.doi.org/10.3390/rs12071092.

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Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learning approach to tackle these problems. We use transfer learning to compensate for the lack of data, and data augmentation to tackle varying scale and orientation. To reduce network size, we use a novel unsupervised learning approach based on k-means clustering, applied to all parts of the network: most network reduction methods use computationally expensive supervised learning methods, and apply only to the convolutional or fully connected layers, but not both. In experiments, we set new standards in classification accuracy on four remote-sensing and two scene-recognition image datasets.
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29

Mihaylov, Oleg, Galina Nikolcheva, and Peter Popov. "SETUP GENERATION USING NEURAL NETWORKS." CBU International Conference Proceedings 5 (September 24, 2017): 1169–74. http://dx.doi.org/10.12955/cbup.v5.1090.

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The article presents an unsupervised learning algorithm that groups technological features in a setup for machining process. Setup generation is one of the most important tasks in automated process planning and in fixture configuration. A setup is created based on approach direction of the features. The algorithm proposed in this work generates a neural network that determines the setup each feature belongs to, and the number of setups generated is minimal. This algorithm, unlike others, is not influenced by the order of the input sequence. Parallel implementation of the algorithm is straightforward and can significantly increase the computational performance.
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30

Gu, Ming. "The Algorithm of Quadratic Junction Neural Network." Applied Mechanics and Materials 462-463 (November 2013): 438–42. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.438.

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Neural network with quadratic junction was described. Structure, properties and unsupervised learning rules of the neural network were discussed. An ART-based hierarchical clustering algorithm using this kind of neural networks was suggested. The algorithm can determine the number of clusters and clustering data. A 2-D artificial data set is used to illustrate and compare the effectiveness of the proposed algorithm and K-means algorithm.
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31

Zhuang, Chengxu, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, and Daniel L. K. Yamins. "Unsupervised neural network models of the ventral visual stream." Proceedings of the National Academy of Sciences 118, no. 3 (January 11, 2021): e2014196118. http://dx.doi.org/10.1073/pnas.2014196118.

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Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today’s best supervised methods and that the mapping of these neural network models’ hidden layers is neuroanatomically consistent across the ventral stream. Strikingly, we find that these methods produce brain-like representations even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. We also find that semisupervised deep contrastive embeddings can leverage small numbers of labeled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results illustrate a use of unsupervised learning to provide a quantitative model of a multiarea cortical brain system and present a strong candidate for a biologically plausible computational theory of primate sensory learning.
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32

Supatman, Supatman, and Sri Ayem. "UMKM Clusterization with Unsupervised Neural Networks Method for Accounting by Business Capital." TAMANSISWA INTERNATIONAL JOURNAL IN EDUCATION AND SCIENCE 2, no. 1 (October 27, 2020): 33–39. http://dx.doi.org/10.30738/tijes.v2i1.7698.

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UMKM menurut pasal (6) UU nomor 20 tahun 2008 berdasarkan asset dan omset dibagi menjadi tiga kriteria yaitu UMi (Usaha Mikro), UK (Usaha Kecil) dan UM (Usaha Menengah). Sementara itu variabel dalam laporan BPS terkait UMKM meliputi Unit Usaha, Tenaga Kerja, PDB atas usaha yang berlaku, PDB atas dasar harga konstan 2000, Total Ekspor Non Migas, Investasi atas dasar harga berlaku, Investasi atas dasar harga konstan 2000. Sehingga pendekatan untuk melakukan kriteria berdasarkan asset dan omset relatif lemah mengingat secara rinci terdapat 7 variabel pendukung kriteria (berdasarkan data BPS).Unsupervised Neural Networks merupakan metode klusterisasi pembelajaran mandiri yang dapat melakukan klaterisasi data berdasarkan jarak eucledian data. SOM-Kohonen merupakan salah satu jenis Unsupervised Neural Networks yang digunakan untuk klasterisasi UMKM pada penelitian ini. Berdasarkan pengujian menggunakan data UMKM tahun 2010 – 2018, dengan parameter pelatihan alfa : 0.1, decalfa 0.2, iterasi 500 diperoleh hasil bahwa kluster UMKM terkluster menjadi 2 dengan perincian Umi tetap sebagai kluster Umi, sedangkan UK dan UM menggabung menjadi satu kluster.Berdasarkan hasil klusterisasi menggunakan unsupervised neural networks dengan SOM-Kohonen yaitu dua klaster, maka direkomendasikan pemodalan dibagi menjadi dua sesuai dengan klusternya. Keywords: Accounting, Business, Clusterization, UMKM, Unsupervised, Neural Networks, SOM-Kohonen.
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33

Sharma, Rachita, and Sanjay Kumar Dubey. "ANALYSIS OF SOM & SOFM TECHNIQUES USED IN SATELLITE IMAGERY." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (June 21, 2018): 563–65. http://dx.doi.org/10.24297/ijct.v4i2c1.4181.

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This paper describes the introduction of Supervised and Unsupervised Techniques with the comparison of SOFM (Self Organized Feature Map) used for Satellite Imagery. In this we have explained the way of spatial and temporal changes detection used in forecasting in satellite imagery. Forecasting is based on time series of images using Artificial Neural Network. Recently neural networks have gained a lot of interest in time series prediction due to their ability to learn effectively nonlinear dependencies from large volume of possibly noisy data with a learning algorithm. Unsupervised neural networks reveal useful information from the temporal sequence and they reported power in cluster analysis and dimensionality reduction. In unsupervised learning, no pre classification and pre labeling of the input data is needed. SOFM is one of the unsupervised neural network used for time series prediction .In time series prediction the goal is to construct a model that can predict the future of the measured process under interest. There are various approaches to time series prediction that have been used over the years. It is a research area having application in diverse fields like weather forecasting, speech recognition, remote sensing. Advances in remote sensing technology and availability of high resolution images in recent years have motivated many researchers to study patterns in the images for the purpose of trend analysis
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PAVLIDIS, N. G., D. K. TASOULIS, V. P. PLAGIANAKOS, and M. N. VRAHATIS. "COMPUTATIONAL INTELLIGENCE METHODS FOR FINANCIAL TIME SERIES MODELING." International Journal of Bifurcation and Chaos 16, no. 07 (July 2006): 2053–62. http://dx.doi.org/10.1142/s0218127406015891.

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In this paper, the combination of unsupervised clustering algorithms with feedforward neural networks in exchange rate time series forecasting is studied. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Combining this input space partitioning methodology with feedforward neural networks acting as local predictors for each identified cluster helps alleviate the problem of nonstationarity frequently encountered in real-life applications. An improvement in the one-step-ahead forecasting accuracy was achieved compared to a global feedforward neural network model for the time series of the exchange rate of the German Mark to the US Dollar.
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35

Crawford, Eric, and Joelle Pineau. "Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3412–20. http://dx.doi.org/10.1609/aaai.v33i01.33013412.

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There are many reasons to expect an ability to reason in terms of objects to be a crucial skill for any generally intelligent agent. Indeed, recent machine learning literature is replete with examples of the benefits of object-like representations: generalization, transfer to new tasks, and interpretability, among others. However, in order to reason in terms of objects, agents need a way of discovering and detecting objects in the visual world - a task which we call unsupervised object detection. This task has received significantly less attention in the literature than its supervised counterpart, especially in the case of large images containing many objects. In the current work, we develop a neural network architecture that effectively addresses this large-image, many-object setting. In particular, we combine ideas from Attend, Infer, Repeat (AIR), which performs unsupervised object detection but does not scale well, with recent developments in supervised object detection. We replace AIR’s core recurrent network with a convolutional (and thus spatially invariant) network, and make use of an object-specification scheme that describes the location of objects with respect to local grid cells rather than the image as a whole. Through a series of experiments, we demonstrate a number of features of our architecture: that, unlike AIR, it is able to discover and detect objects in large, many-object scenes; that it has a significant ability to generalize to images that are larger and contain more objects than images encountered during training; and that it is able to discover and detect objects with enough accuracy to facilitate non-trivial downstream processing.
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36

Damilola, Samson. "A Review of Unsupervised Artificial Neural Networks with Applications." International Journal of Computer Applications 181, no. 40 (February 15, 2019): 22–26. http://dx.doi.org/10.5120/ijca2019918425.

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Saunders, Daniel J., Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, and Robert Kozma. "Locally connected spiking neural networks for unsupervised feature learning." Neural Networks 119 (November 2019): 332–40. http://dx.doi.org/10.1016/j.neunet.2019.08.016.

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Ranjard, Louis, and Howard A. Ross. "Unsupervised bird song syllable classification using evolving neural networks." Journal of the Acoustical Society of America 123, no. 6 (June 2008): 4358–68. http://dx.doi.org/10.1121/1.2903861.

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Tagliaferri, R., N. Capuano, and G. Gargiulo. "Automated labeling for unsupervised neural networks: a hierarchical approach." IEEE Transactions on Neural Networks 10, no. 1 (1999): 199–203. http://dx.doi.org/10.1109/72.737509.

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Sun, Yanan, Gary G. Yen, and Zhang Yi. "Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations." IEEE Transactions on Evolutionary Computation 23, no. 1 (February 2019): 89–103. http://dx.doi.org/10.1109/tevc.2018.2808689.

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Kumar, V. P., and E. S. Manolakos. "Unsupervised statistical neural networks for model-based object recognition." IEEE Transactions on Signal Processing 45, no. 11 (1997): 2709–18. http://dx.doi.org/10.1109/78.650097.

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Dosovitskiy, Alexey, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox. "Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 9 (September 1, 2016): 1734–47. http://dx.doi.org/10.1109/tpami.2015.2496141.

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Storrs, Katherine R., and Roland W. Fleming. "Unsupervised Neural Networks Learn Idiosyncrasies of Human Gloss Perception." Journal of Vision 19, no. 10 (September 6, 2019): 213. http://dx.doi.org/10.1167/19.10.213.

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Barreto, Guilherme de A., and Aluizio F. R. Araújo. "Fast learning of robot trajectories via unsupervised neural networks." IFAC Proceedings Volumes 32, no. 2 (July 1999): 5076–81. http://dx.doi.org/10.1016/s1474-6670(17)56864-0.

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Kosko, B. A. "Structural stability of unsupervised learning in feedback neural networks." IEEE Transactions on Automatic Control 36, no. 7 (July 1991): 785–92. http://dx.doi.org/10.1109/9.85058.

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Likhovidov, Victor. "Variational Approach to Unsupervised Learning Algorithms of Neural Networks." Neural Networks 10, no. 2 (March 1997): 273–89. http://dx.doi.org/10.1016/s0893-6080(96)00051-2.

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Emami, Ebrahim, Touqeer Ahmad, George Bebis, Ara Nefian, and Terry Fong. "Crater Detection Using Unsupervised Algorithms and Convolutional Neural Networks." IEEE Transactions on Geoscience and Remote Sensing 57, no. 8 (August 2019): 5373–83. http://dx.doi.org/10.1109/tgrs.2019.2899122.

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Munoz-Martin, Irene, Stefano Bianchi, Giacomo Pedretti, Octavian Melnic, Stefano Ambrogio, and Daniele Ielmini. "Unsupervised Learning to Overcome Catastrophic Forgetting in Neural Networks." IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 5, no. 1 (June 2019): 58–66. http://dx.doi.org/10.1109/jxcdc.2019.2911135.

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Mishra, Bhabani Shankar Prasad, Om Pandey, Satchidananda Dehuri, and Sung-Bae Cho. "Unsupervised Functional Link Artificial Neural Networks for Cluster Analysis." IEEE Access 8 (2020): 169215–28. http://dx.doi.org/10.1109/access.2020.3024111.

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Wen, C. M., S. L. Hung, C. S. Huang, and J. C. Jan. "Unsupervised fuzzy neural networks for damage detection of structures." Structural Control and Health Monitoring 14, no. 1 (2007): 144–61. http://dx.doi.org/10.1002/stc.116.

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