K-means learning algorithm
K-means improvement by dynamic pre-aggregates
... data learning algorithm, called ...an algorithm that pre-calculates and stores intermediate results, called dynamic pre-aggregates, to be reused in subsequent ...extended k-means ...
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Controlling evolution by means of machine learning
... Three problems are considered: the Royal Road problem [17], a GA-deceptive problem [31], and a combinatorial optimization problem [11]. 3.1 Experimental settings The evolutionary algorithm is a standard GA [7] ...
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Learning slosh dynamics by means of data
... Ladicky [ 23 ] presented a GPU implementation consisting of a forest regressor [ 4 ], trained with a large set of videos to extract pairs of snapshots, able to predict the state of each particle in the following time ...
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Unsupervised Speech Unit Discovery Using K-means and Neural Networks
... CNN Learning For initialization, the k-means algorithm uses frames of log F-bank coefficients as input and each input feature window is concatenated with its 6 neighborhood ...the ...
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Generalized k-means based clustering for temporal data under time warp
... machine learning tasks, to extract groups of time series and highlight the main underlying ...[KLT03]. k-means-based clustering, viz. standard k-means, k-means++, and all ...
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K-means+: an autonomous clustering algorithm
... supervised learning methods, the unsupervised learning methods, known as clustering methods, do not have the knowledge of the classes to which the objects can be ...the learning processes; ...
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K-means improvement by dynamic pre-aggregates
... data learning algorithm, called ...an algorithm that pre-calculates and stores intermediate results, called dynamic pre-aggregates, to be reused in subsequent ...extended k-means ...
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Median evidential c-means algorithm and its application to community detection
... The table Tab. 2 lists the indices for evaluating the different methods. Bold entries in each column of this table (and also other tables in the following) indicate that the results are significant as the top performing ...
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A k-segments algorithm for finding principal curves
... the algorithm for many di erent numbers of segments is time onsuming, we propose to use an in
remental strategy ...with k = 1 segment and use the sear
h method des
ribed in the previous subse
tions to nd ...
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Structured Cooperative Learning as a Means for Improving Average Achievers' Mathematical Learning in Fractions
... In the high-structure condition (n = 54, k =18, l = 9), materials were divided among the pupils in each triad (i.e., one ruler per person), reinforcing the positive resource interdependence. Pupils worked alone ...
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The global k-means clustering algorithm
... global k-means approa
h gives rise to performan
e signi antly better than when starting with all enters at the same time initialized using the k-d tree method, and (b) restri
ting the insertion lo
...
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Identification non-supervisée de pseudo-phones à l'aide de k-means et de réseaux convolutifs
... {celine.manenti, thomas.pellegrini, julien.pinquier}@irit.fr Résumé – Retrouver de manière non supervisée des unités sous-lexicales et identifier des pseudo-mots dans la parole est un problème qui intéresse actuellement ...
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Sparse k-means for mixed data via group-sparse clustering
... We provide a complete procedure for simultaneously clustering mixed data, and selecting the most relevant features for the clustering. This procedure is illus- trated on a real dataset and proves to be efficient. Further ...
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NMR metabolic analysis of samples using fuzzy K-means clustering
... S104 that can be observed in the PCA representation of membership values. For both datasets, F-KM classification based on the top membership values resulted in better quality of clustering results based on both Jaccard ...
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Multi-criteria Search Algorithm: An Efficient Approximate K-NN Algorithm for Image Retrieval
... a k-NN search for each descriptor of the query, which is problematic when a large number of descriptors per image is ...single k-NN search for the query image by com- paring the ...
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Applying subclustering and Lp distance in Weighted K-Means with distributed centroids
... Our results suggest that MWK-DC generally outperforms WK-DC as well as its subclustering version, WK-DC S, in terms of both cluster recovery, measured 355 using the adjusted Rand index and the number of completed ...
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An Inertial Newton Algorithm for Deep Learning
... our algorithm to the classical stochastic gradient descent (SGD) algorithm, and both the very popular ADAGRAD [13] and ADAM [14] ...each algorithm is initialized with the same random weights ...
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Using k-means for redundancy and inconsistency detection: application to industrial requirements
... of k-means algorithm for redun- dancy and inconsistency detection in a new context, which is Require- ments Engineering ...the k-means ...the k-means clustering ...
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Comparison of K-means and GMM methods for contextual clustering in HSM
... [6] Martínez-Arellano, G., Terrazas, G., Benardos, P., & Ratchev, S., (2018). In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis. In: 8th CIRP Conference on High ...
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An Inertial Newton Algorithm for Deep Learning
... 100(θ 2 − |θ 1 |) 2 + |1 − θ 1 |. This function has a V-shaped valley, and a unique critical point at (1, 1) which is also the global minimum. Starting from the point ( −1, 1.5) (the black cross), we apply INDIAN with ...
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