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Learning boxes in high dimension [chapter]

Amos Beimel, Eyal Kushilevitz
1997 Lecture Notes in Computer Science  
In this case only equivalence queries are used, i.e. we learn this class in the on-line model 20]. This result generalizes the learnability of O(1)-DNF and of boxes in O(1) dimensions.  ...  in the union is limited to O(1) (but the number of dimensions in not restricted).  ... 
doi:10.1007/3-540-62685-9_2 fatcat:fbtgnwso6nc6hm5inlb47zuiry

Learning unions of high-dimensional boxes over the reals

Amos Beimel, Eyal Kushilevitz
2000 Information Processing Letters  
In [4] an algorithm is presented that exactly learns (using membership queries and equivalence queries) several classes of unions of boxes in high dimension over finite discrete domains.  ...  Specifically, we learn unions of t disjoint n-dimensional boxes over the reals in time polynomial in n and t, and unions of O(log n) (possibly intersecting) n-dimensional boxes over the reals in time polynomial  ...  boxes in high dimension over finite discrete domains (in the exact learning model).  ... 
doi:10.1016/s0020-0190(00)00024-7 fatcat:zjix2np7o5ed3jzt44winumm2q

Page 2 of Journal of Comparative Psychology Vol. 48, Issue 1 [page]

1955 Journal of Comparative Psychology  
The high hurdles were 1% in. high and the low ones 4 in. high. Any given pair of these goal boxes could be presented on a given trial.  ...  Half the Ss in this group always had high hurdles in both goal boxes on every trial, and half had low hurdles. High-low hurdle reversal constant (HRC).  ... 

Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement Learning [article]

Sean Gillen, Katie Byl
2021 arXiv   pre-print
In this work we argue that the dimensionality of this subspace is captured by tools from fractal geometry, namely various notions of a fractional dimension.  ...  A key limitation in using various modern methods of machine learning in developing feedback control policies is the lack of appropriate methodologies to analyze their long-term dynamics, in terms of making  ...  With these factors in mind, we introduce two box mesh dimensions.  ... 
arXiv:2012.11662v2 fatcat:cv6zthnmmfa6daggca4utvj6qq

Improved Feature Extraction Method for Sound Recognition Applied to Automatic Sorting of Recycling Wastes

Tatsuji Munaka, Farzad Samie, Lars Bauer, Jörg Henkel
2020 Journal of Information Processing  
However, the sounds of recycling wastes have features of frequency components found in higher dimensions.  ...  In many types of research for voice recognition, Mel Frequency Cepstral Coefficient (MFCC) has been used as an algorithm for extracting features used for machine learning Support Vector Machines (SVMs)  ...  MFCC is the information extracted from low-dimensional 12 dimensions in which voice features appear from the cepstrum in Fig. 6 .  ... 
doi:10.2197/ipsjjip.28.658 fatcat:4mj7we7jtnfnjoo375bzyfzwvi

Think Outside the Box! [From the Editor]

Christian Jutten
2021 IEEE Signal Processing Magazine  
They are focused on the processing of data, especially large data in high dimension, using signal processing and machine learning methods.  ...  He then developed the concept of curvilinear component analysis, which extends the Kohonen's maps to huge-dimension data and implicitly considers high-dimension data are embedded in a low-dimension manifold  ... 
doi:10.1109/msp.2021.3086574 fatcat:22f6lpsk65fdphdxfozs2fthee

BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy [chapter]

Liang Qu, Qiqi Duan, Jian Yang, Shi Cheng, Ruiqi Zheng, Yuhui Shi
2020 Lecture Notes in Computer Science  
Brain storm optimization algorithms (BSO) have shown great potential in many global black-box optimization problems.  ...  It is inspired by the new ideas generating process of brain storm in which the participators propose their own ideas by cooperatively learning other participators' ideas.  ...  -High-dimension: The dimension D of the candidate solution x is typically larger than or equal to 1000 (i.e. D ≥ 1000).  ... 
doi:10.1007/978-3-030-53956-6_22 fatcat:wcjlsqmb4fbqhofzjkcdienhfi

Enhanced Kriging Models within a Bayesian Optimization Framework, to Handle both continuous and Categorical Inputs

P. Saves, N. Bartoli, T. Lefebre, Y. Diouane, J. Morlier
2021 Zenodo  
SIAM Conference on Computational Science and Engineering MS130 Derivative-Free Optimization Methods for Solving Expensive Global Black-Box Problems - Part II of II  ...  "Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction". Structural and Multidisciplinary Optimization i.  ...  Constraints  High-Dimension  Mixed integer  Continuous relaxation Garrido-Merchán and Hernández-Lobato Constraints  High-Dimension  Mixed integer  E. C. Garrido-Merchán, and D.  ... 
doi:10.5281/zenodo.5743339 fatcat:u36j2gkuzjf2vbnvsjk4zkwxkq

Capacity and Bias of Learned Geometric Embeddings for Directed Graphs

Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L. Clarkson, Andrew McCallum
2021 Neural Information Processing Systems  
performance saturation common to other geometric models in high dimensions.  ...  In this work, we introduce a novel variant of box embeddings that uses a learned smoothing parameter to achieve better representational capacity than vector models in low dimensions, while also avoiding  ...  Some of the work reported here was performed using high performance computing equipment obtained under a grant from the Collaborative R&D Fund managed by the Massachusetts Technology Collaborative.  ... 
dblp:conf/nips/BoratkoZMVCM21 fatcat:ewju6ovgdnbvnmf7syeuiztxya

Generative Evolutionary Strategy For Black-Box Optimizations [article]

Changhwi Park, Seong Ryeol Kim, Young-Gu Kim, Dae Sin Kim
2022 arXiv   pre-print
However, their capability in high-dimensional search space is still limited.  ...  Among them, black-box optimization in high-dimensional space is particularly challenging. Recent neural network-based black-box optimization studies have shown noteworthy achievements.  ...  Similarly, we guess that the critic network in GEO learns low-dimensional manifolds in the high-dimensional space.  ... 
arXiv:2205.03056v3 fatcat:g24qousoczaqdo45abnt52wx2q

Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-based Techniques

Peng, Zhang, Chen, Zeng
2019 Applied Sciences  
Dimension measurement is of utmost importance in the logistics industry. This work studies a hand-held structured light vision system for boxes.  ...  This system measures dimension information through laser triangulation and deep learning using only two laser-box images from a camera and a cross-line laser projector.  ...  Introduction The dimensional inspection of 3D objects is an important feature in many intelligent systems.  ... 
doi:10.3390/app10010026 fatcat:3jszvbeszzecdovdfx7xhspoau

A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge [article]

Jianning Li, Antonio Pepe, Christina Gsaxner, Gord von Campe, Jan Egger
2020 arXiv   pre-print
used to generate the bounding box of the defected region in the original high-resolution skull.  ...  The approach generates high-quality implants in two steps: First, an encoder-decoder network learns a coarse representation of the implant from down-sampled, defective skulls; The coarse implant is only  ...  X B and Y B are dimensions of the bounding box in x/y volume axis.  ... 
arXiv:2006.12449v2 fatcat:t3nk43oxxbafnmocww3nxnvhai

From Hate to Love: How Learning Can Change Affective Responses to Touched Materials [chapter]

Müge Cavdan, Alexander Freund, Anna-Klara Trieschmann, Katja Doerschner, Knut Drewing
2020 Lecture Notes in Computer Science  
These points to differences in the strength of perceptuo-affective relations, which we discuss in terms of hard-wired versus learned connections.  ...  In the learning phase, participants haptically explored materials that are either very rough or very fine-grained while they simultaneously watched positive or negative stimuli, respectively, from the  ...  high factor values in any of the other dimensions (fluidity, fibrousness, heaviness, deformability).  ... 
doi:10.1007/978-3-030-58147-3_7 fatcat:rarknqwovjbthp5cev2t4bm7ja

Learning to Learn by Zeroth-Order Oracle [article]

Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh
2020 arXiv   pre-print
Our learned optimizer outperforms hand-designed algorithms in terms of convergence rate and final solution on both synthetic and practical ZO optimization tasks (in particular, the black-box adversarial  ...  In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules.  ...  Furthermore, it is not suitable for solving black-box optimization problems of high dimensions.  ... 
arXiv:1910.09464v2 fatcat:2hdpy6qqtvf4vbxz3swkruydcm

Signal Pattern Recognition Based on Fractal Features and Machine Learning

Chang-Ting Shi
2018 Applied Sciences  
Box fractal dimension, Katz fractal dimension, Higuchi fractal dimension, Petrosian fractal dimension, and Sevcik fractal dimension are extracted from eight different modulation signals for signal pattern  ...  In this paper, we conduct a systematic research study by using the fractal dimension as the feature of modulation signals.  ...  The common one-dimensional fractal dimensions are box dimension, Hausdorff-Besicovitch dimension, Sevcik fractal dimension, Katz dimension, Higuchi dimension, and Petrosian dimension.  ... 
doi:10.3390/app8081327 fatcat:7b3n7jm7hncrxegz5begsbclvq
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