Unsupervised and Supervised Learning Approaches Together for Microarray Analysis

Recent unsupervised learning approaches have explored the feasibility of semantic analysis and interpretation of imagery using Emergent Language (EL) models. As EL requires some form of numerical embedding as input, it remains unclear which type is required in order for the EL to properly capture key semantic concepts associated with a given domain. In this paper, we compare unsupervised and supervised approaches for generating embeddings across two experiments. In Experiment 1, data are produced using a single-agent simulator. In each episode, a goal-driven agent attempts to accomplish a number of tasks in a synthetic cityscape environment which includes houses, banks, theaters and restaurants. In Experiment 2, a comparatively smaller dataset is produced where one or more objects demonstrate various types of physical motion in a 3D simulator environment. We investigate whether EL models generated from embeddings of raw pixel data produce expressions that capture key latent concepts (i.e. an agent’s motivations or physical motion types) in each environment. Our initial experiments show that the supervised learning approaches yield embeddings and EL descriptions that capture meaningful concepts from raw pixel inputs. Alternatively, embeddings from an unsupervised learning approach result in greater ambiguity with respect to latent concepts.

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Performance Estimation and Analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification

Intelligent Computing and Applications - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-15-5566-4_12 ◽

2020 ◽

pp. 125-141

Author(s):

Gopal Chandra Jana ◽

Shivam Shukla ◽

Divyansh Srivastava ◽

Anupam Agrawal

Keyword(s):

Supervised Learning ◽

Motor Imagery ◽

Performance Estimation ◽

Learning Approaches ◽

Eeg Signals

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Supervised Learning-Based Fast, Stealthy, and Active NAT Device Identification Using Port Response Patterns

Symmetry ◽

10.3390/sym12091444 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1444

Author(s):

Seungwoon Lee ◽

Si Jung Kim ◽

Jungtae Lee ◽

Byeong-hee Roh

Keyword(s):

Supervised Learning ◽

Response Patterns ◽

Learning Approaches ◽

Security Issue ◽

Ip Address ◽

Device Identification ◽

Address Translation ◽

Proposed Model ◽

Network Operations ◽

Network Administrators

Although network address translation (NAT) provides various advantages, it may cause potential threats to network operations. For network administrators to operate networks effectively and securely, it may be necessary to verify whether an assigned IP address is using NAT or not. In this paper, we propose a supervised learning-based active NAT device (NATD) identification using port response patterns. The proposed model utilizes the asymmetric port response patterns between NATD and non-NATD. In addition, to reduce the time and to solve the security issue that supervised learning approaches exhibit, we propose a fast and stealthy NATD identification method. The proposed method can perform the identification remotely, unlike conventional methods that should operate in the same network as the targets. The experimental results demonstrate that the proposed method is effective, exhibiting a F1 score of over 90%. With the efficient features of the proposed methods, we recommend some practical use cases that can contribute to managing networks securely and effectively.

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Disagreement based semi-supervised learning approaches with belief functions

Knowledge-Based Systems ◽

10.1016/j.knosys.2019.105426 ◽

2020 ◽

Vol 193 ◽

pp. 105426

Author(s):

Hongshun He ◽

Deqiang Han ◽

Jean Dezert

Keyword(s):

Supervised Learning ◽

Belief Functions ◽

Learning Approaches

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Content-based image retrieval based on supervised learning and statistical-based moments

Modern Physics Letters B ◽

10.1142/s0217984919502130 ◽

2019 ◽

Vol 33 (19) ◽

pp. 1950213 ◽

Cited By ~ 9

Author(s):

Vibhav Prakash Singh ◽

Rajeev Srivastava ◽

Yadunath Pathak ◽

Shailendra Tiwari ◽

Kuldeep Kaur

Keyword(s):

Image Retrieval ◽

Supervised Learning ◽

Texture Features ◽

Ground Truth ◽

Local Binary Patterns ◽

Content Based Image Retrieval ◽

Support Vector ◽

Svm Classifier ◽

Learning Approaches ◽

Query Image

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

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