![]() This parameter can again 2 types of values, percentage and absolute. Max_df looks at how many documents contain the word and if it exceeds the max_df threshold then it is eliminated from the sparse matrix. These words could be like the word ‘the’ that occur in every document and does not provide and valuable information to our text classification or any other machine learning model and can be safely ignored. Similar to min_df, we can ignore words which occur frequently. Max_df stands for maximum document frequency. Zhou Z, Chen X, Li E, Zeng L, Luo K, Zhang J (2019) Edge intelligence: paving the last mile of artificial intelligence with edge computing.3. ACM Trans Intell Syst Technol (TIST) 10(2):1–19 Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. Satyanarayanan M (2017) The emergence of edge computing. Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Association for Computing Machinery, New York, pp 234–250. In: Proceedings of the fall joint computer conference, AFIPS’62 (Fall), 4–. ![]() Salton G (1962) Some experiments in the generation of word and document associations. Sahlgren M, Kanerva P (2008) Permutations as a means to encode order in word space. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543 Pennington J, Socher R, Manning CD (2014) GloVe: global vectors for word representation. ![]() ![]() In: Proceedings of 2nd international conference on learning representations Norouzi M, Mikolov T, Bengio S, Singer Y, Shlens J, Frome A, Corrado G, Dean J (2014) Zero-shot learning by convex combination of semantic embeddings. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. McMahan HB, Moore E, Ramage D, y Arcas BA (2016) Federated learning of deep networks using model averaging. In: International conference on machine learning, pp 1188–1196 Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Foundations of real-world intelligence. Uesaka Y, Kanerva P, Asoh H, Karlgren J, Sahlgren M (2001) From words to understanding. In: Proceedings of the 22nd annual conference of the cognitive science society, vol 1036. Kanerva P, Kristofersson J, Holst A (2000) Random indexing of text samples for latent semantic analysis. Hu YC, Patel M, Sabella D, Sprecher N, Young V (2015) Mobile edge computing-a key technology towards 5G. Association for Computing Machinery, New York, pp 855–864 ![]() In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD’16. Grover A, Leskovec J (2016) Node2vec: scalable feature learning for networks. In: Advances in neural information processing systems, pp 2121–2129 CoRR abs/1810.04805, įrome A, Corrado GS, Shlens J, Bengio S, Dean J, Ranzato M, Mikolov T (2013) Devise: a deep visual-semantic embedding model. Experimental results on natural language as well as graph datasets show that this may be a promising new direction.ĭevlin J, Chang M, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. Where joint learning is not possible, we propose novel semantic vector translation algorithms to enable semantic query across multiple edge locations, each with its own semantic vector space. Specifically, for scenarios where multiple edge locations can engage in joint learning, we adapt the proposed federated learning techniques for semantic vector embedding. In this chapter, we address this gap by reviewing novel unsupervised algorithms for learning and applying semantic vector embeddings in a variety of distributed settings. Hence, the applicability of state-of-the-art embedding approaches is limited to freely shared datasets, leaving out applications with sensitive or mission-critical data. However, in many scenarios, data are distributed across multiple edge locations and cannot be aggregated due to a variety of constraints. State-of-the-art embedding approaches assume all data are available at a centralized location. A key application enabled by such techniques is the ability to measure semantic similarity between given data samples and find similar data points via encoding comparison. Semantic vector embedding techniques have proven useful in developing mathematical relationships of non-numeric data such as text. ![]()
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