Unsupervised Learning Methods For Turkish Natural Language Processing.

Unsupervised machine learning in 5G networks for low latency communications Balevi, Eren; Gitlin, Richard D. (2018-02-02) © 2017 IEEE.This paper incorporates fog networking into heterogeneous cellular networks that are composed of a high power node (HPN) and many low power nodes (LPNs). The locations of the fog nodes that are upgraded from LPNs are specified by modifying the unsupervised soft-clustering machine learning algorithm with the ultimate aim of reducing latency. The clusters are constructed accordingly so that the leader of each cluster becomes a fog node. The proposed approach significantly reduces the latency with ...
Unsupervised Deep Learning for Subspace Clustering SEKMEN, ali; Koku, Ahmet Buğra; PARLAKTUNA, Mustafa; ABDULMALEK, Ayad; VANAMALA, Nagendrababu (2017-12-14) This paper presents a novel technique for the segmentation of data W = [w(1) . . . w(N)] subset of R-D drawn from a union U = boolean OR(M)(i=1) S-i of subspaces {S-i}(i=1)(M). First, an existing subspace segmentation algorithm is used to perform an initial data clustering {C-i}(i=1)(M), where C-i = {w(i1) . . . w(ik)} subset of W is the set of data from the ith cluster. Then, a local subspace LSi is matched for each C-i and the distance d(ij) between LSi and each point w(ij) is an element of C-i is compute...
Unsupervised Metric Learning for Face Identification in TV Video Cinbiş, Ramazan Gökberk; Schmid, Cordelia (2011-01-01) The goal of face identification is to decide whether two faces depict the same person or not. This paper addresses the identification problem for face-tracks that are automatically collected from uncontrolled TV video data. Face-track identification is an important component in systems that automatically label characters in TV series or movies based on subtitles and/or scripts: it enables effective transfer of the sparse text-based supervision to otherfaces. We show that, without manually labeling any examp...
Unsupervised Learning of Object Affordances for Planning in a Mobile Manipulation Platform Ugur, Emre; Şahin, Erol; Oztop, Erhan (2011-05-13) In this paper, we use the notion of affordances, proposed in cognitive science, as a framework to propose a developmental method that would enable a robot to ground symbolic planning mechanisms in the continuous sensory-motor experiences of a robot. We propose a method that allows a robot to learn the symbolic relations that pertain to its interactions with the world and show that they can be used in planning. Specifically, the robot interacts with the objects in its environment using a pre-coded repertoire...
Unsupervised Morphological Segmentation Using Neural Word Embeddings Ustun, Ahmet; CAN BUĞLALILAR, BURCU (2016-10-12) We present a fully unsupervised method for morphological segmentation. Unlike many morphological segmentation systems, our method is based on semantic features rather than orthographic features. In order to capture word meanings, word embeddings are obtained from a two-level neural network [11]. We compute the semantic similarity between words using the neural word embeddings, which forms our baseline segmentation model. We model morphotactics with a bigram language model based on maximum likelihood estimat...

Citation Formats

R. Çakıcı, “Unsupervised Learning Methods For Turkish Natural Language Processing.,” 2014. Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/61862.