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Nonlinear Dimensionality Reduction - Temple University

https://cis.temple.edu/~latecki/Courses/AI-Fall12/Lectures/NLDimRed.pdf

Linear Dimensionality Reduction: Based on a linear projection of the data

Latest News | Temple University | Temple University

https://www.temple.edu/news

When a new immunization to protect newborns from respiratory syncytial virus (RSV) became available […]

John Henry – American Icons

https://sites.temple.edu/americanicons/tag/john-henry/

The myth of John Henry sacrificing his life to compete with a mechanical drill reminds me of Martin Luther King Jr., who also devoted his life to contribute to the cause of fighting against a powerful social system with racial inequality.

Ch. 2: Linear Discriminants slides based on Stephen Marsland, Machine ...

https://cis-linux1.temple.edu/~latecki/Courses/AI-Fall10/Lectures/ch2.ppt

* * * * * * * * * * * * * * * * * * * * Figure 1: scatter(1:20,10+(1:20)+2*randn(1,20),'k','filled'); a=axis; a(3)=0; axis(a); * Figure 1: scatter(1:20,10+(1:20)+2 ...

Joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach

https://cis.temple.edu/~wu/research/publications/Publication_files/ICDE2024_Xu.pdf

Abstract—In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a ...

An introduction to Support Vector Machines

https://cis-linux1.temple.edu/~latecki/Courses/AI-Fall11/Lectures/ch5SVM.ppt

Outline What do we mean with classification, why is it useful Machine learning- basic concept Support Vector Machines (SVM) Linear SVM – basic terminology and some formulas Non-linear SVM – the Kernel trick An example: Predicting protein subcellular location with SVM Performance measurments Classification Everyday, all the time we classify things. Eg crossing the street: Is there a car ...

Prenatal maternal Inflammation, childhood cognition and adolescent ...

https://sites.temple.edu/ellmanlab/files/2024/12/Pike-et-al-2024.pdf

Background: Accumulating evidence indicates that higher prenatal maternal inflammation is associated with increased depression risk in adolescent and adult-aged offspring. Prenatal maternal inflammation (PNMI) may increase the likelihood for offspring to have lower cognitive performance, which, in turn, may heighten risk for depression onset. Therefore, this study explored the potential ...

Reinforcement Learning-based Dual-Identity Double Auction in ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/Reinforcement_Learning-based_Dual-Identity_Double_Auction_in_Personalized_Federated_Learning.pdf

Reinforcement Learning-based Dual-Identity Auction in Personalized Federated Juan Li, Member, IEEE, Zishang Chen, Tianzi Wu, Fellow, IEEE, Yanmin

stoj-1.qxd - Temple University

https://cis.temple.edu/~wu/teaching/Spring%202013/handoff.pdf

1.1 INTRODUCTION Mobility is the most important feature of a wireless cellular communication system. Usu-ally, continuous service is achieved by supporting handoff (or handover) from one cell to another. Handoff is the process of changing the channel (frequency, time slot, spreading code, or combination of them) associated with the current connection while a call is in progress. It is often ...

Detecting, Localizing, and Tracking an Unknown Number of Moving Targets ...

https://sites.temple.edu/pdames/files/2016/07/DamesTokekarKumarISRR2015.pdf

Abstract Target tracking is a fundamental problem in robotics research and has been the subject of detailed studies over the years. In this paper, we introduce a new formulation, based on the mathematical concept of random finite sets, that allows for tracking an unknown and dynamic number of mobile targets with a team of robots. We show how to employ the Probability Hypothesis Density filter ...