Image compression using JPEG with reduced blocking effects via adaptive down-sampling and self-learning image sparse representation

Amiri, S. Asadi; Hassanpour, Hamid

doi:10.1007/s11042-017-4763-1

Cited by 7 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rapid development of machine learning theory, many first-order inductive learning methods combined with machine learning theory have emerged. For example, nFOIL and tFOIL, proposed by Amiri et al in 2007, combine the plain Bayesian and tree-augmented plain Bayesian algorithms in machine learning with first-order inductive learning [ 10 ]. Bao et al propose a first-order inference method for uncertain RDF knowledge bases [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Construction and Research on Chinese Semantic Mapping Based on Linguistic Features and Sparse Self-Learning Neural Networks

Zhang

Chao

Huang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In this paper, we adopt the algorithms of linguistic feature Rong and sparse self-learning neural network to conduct an in-depth study and analysis of Chinese semantic mapping, which complements the emotion semantic representation ability of traditional word embedding and fully explores the emotion semantic information contained in the text in the task preprocessing stage. We incorporate various semantic features such as lexical information and location information to make the model have richer emotion semantic expression, and the model also uses an attention mechanism to allow various features to interact and abstract deeper contextual internal semantic associations to improve the model's sentiment classification performance. Finally, experiments are conducted on two publicly available English sentiment classification corpora, and the results prove that the model outperforms other comparison models and effectively improves the sentiment classification performance. The model uses deep memory networks and capsule networks to construct a transfer learning framework and effectively leverages the transfer learning properties of capsule networks to transfer knowledge embedded in large-scale labeled data from similar domains to the target domain, improving the classification performance on small data sets. The use of multidimensional combined features compensates for the lack of a one-dimensional feature attention mechanism, while multiple domain category-based attention computation layers are superimposed to obtain deeper domain-specific sentiment feature information. Based on the segmented convolutional neural network, the model first introduces the dependent subtree of relational attributes to obtain the position weights of each word in the sentence, then introduces domain ontology knowledge in the output layer to constrain the extraction results, and conducts experimental comparison through the data set to verify the validity of the model, which ensures the accuracy of the network term entity and relational attribute recognition extraction and makes the knowledge map constructed in this paper. It ensures the accuracy of the extraction rate of the web term entities and relationship attributes and makes the knowledge map constructed in this paper more factual.

show abstract

Section: Related Workmentioning

confidence: 99%