Results 31 to 40 of about 61,624 (324)
Explainable human‐in‐the‐loop healthcare image information quality assessment and selection
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Smart healthcare applications cannot be separated from healthcare data analysis and the interactive interpretability between data and model. A human‐in‐the‐loop active learning approach is introduced to reduce the cost of healthcare data labelling by evaluating the information quality of unlabelled medical data and then screening the high ...
Yang Li, Sezai Ercisli
wiley +1 more source
Journal of Information Systems Engineering and Business IntelligenceBackground: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children.
Cynthia Hayat, Iwan Aang Soenandi
doaj +1 more source
Average Competitive Learning Vector Quantization [PDF]
Communications in Statistics - Simulation and Computation, 2013 We propose a new algorithm for vector quantization:Average Competitive Learning Vector Quantization (ACLVQ). It is a rather simple modification of the classical Competitive Learning Vector Quantization (CLVQ). This new formulation give us similar results for the quantization error to those obtained by the CLVQ and reduce considerably the computation ...
Luis A. Salomón +2 more
openaire +1 more source
On the use of self-organizing maps to accelerate vector quantization
, 2004 Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring input vectors are quantified (or classified) either on the same location or on neighbor ones on a predefined grid.
Anderberg +11 more
core +4 more sources
Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization
Foundations of Computing and Decision Sciences, 2014 .Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge.
Kaden M. +5 more
doaj +1 more source
Bolt: Accelerated Data Mining with Fast Vector Compression
, 2017 Vectors of data are at the heart of machine learning and data mining. Recently, vector quantization methods have shown great promise in reducing both the time and space costs of operating on vectors.
Blalock, Davis W, Guttag, John V
core +1 more source
The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3
IT Journal Research and Development, 2020 The addiction of children to gadgets has a massive influence on their social growth. Thus, it is essential to note earlier on the addiction of children to such technologies.
Okfalisa Okfalisa +4 more
doaj +1 more source
Advanced Functional Materials, EarlyView.An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone +11 more
wiley +1 more source
Greedy vector quantization [PDF]
, 2014 We investigate the greedy version of the $L^p$-optimal vector quantization problem for an $\mathbb{R}^d$-valued random vector $X\!\in L^p$. We show the existence of a sequence $(a_N)_{N\ge 1}$ such that $a_N$ minimizes $a\mapsto\big \|\min_{1\le i\le N-1}
Luschgy, Harald, Pagès, Gilles
core +2 more sources
Steep‐Switching Memory FET for Noise‐Resistant Reservoir Computing System
Advanced Functional Materials, EarlyView.We demonstrate the steep‐switching memory FET with CuInP2S6/h‐BN/α‐In2Se3 heterostructure for application in noise‐resistant reservoir computing systems. The proposed device achieves steep switching characteristics (SSPGM = 19 mV/dec and SSERS = 23 mV/dec) through stabilization between CuInP2S6 and h‐BN.
Seongkweon Kang +6 more
wiley +1 more source