On-line hyperspectral anomaly detection with hypothesis test based model learning

2019 ◽  
Vol 97 ◽  
pp. 15-24
Author(s):  
Ning Ma ◽  
Yu Peng ◽  
Shaojun Wang
Keyword(s):  
Anomaly Detection ◽  
Hypothesis Test ◽  
Model Learning ◽  
On Line

scholarly journals On-line anomaly detection and resilience in classifier ensembles

2013 ◽  
Vol 34 (15) ◽  
pp. 1916-1927 ◽  
Author(s):  
Hesam Sagha ◽  
Hamidreza Bayati ◽  
José del R. Millán ◽  
Ricardo Chavarriaga
Keyword(s):  
Anomaly Detection ◽  
Classifier Ensembles ◽  
On Line

scholarly journals Adaptive Kernel Quantile Regression for Anomaly Detection

2009 ◽  
Vol 13 (3) ◽  
pp. 230-236
Author(s):  
Hiroyuki Moriguchi ◽  
◽  
Ichiro Takeuchi ◽  
Masayuki Karasuyama ◽  
Shin-ichi Horikawa ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Quantile Regression ◽  
Time Series Data ◽  
Data Distribution ◽  
Optimal Solution ◽  
Series Data ◽  
Future Time ◽  
Adaptive Kernel ◽  
On Line

In this paper, we study a problem of anomaly detection from time series-data. We use kernel quantile regression (KQR) to predict the extreme (such as 0.01 or 0.99) quantiles of the future time-series data distribution. It enables us to tell whether the probability of observing a certain time-series sequence is larger than, say, 1 percent or not. In this paper, we develop an efficient update algorithm of KQR in order to adapt the KQR in on-line manner. We propose a new algorithm that allows us to compute the optimal solution of the KQR when a new training pattern is inserted or deleted. We demonstrate the effectiveness of our methodology through numerical experiment using real-world time-series data.

scholarly journals PENGARUH MODEL PEMBELAJARAN STUDENT FACILITATOR AND EXPLAINING TERHADAP HASIL BELAJAR SIMULASI DIGITAL

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Putri Irma Delianti ◽  
Elfi Tasrif ◽  
Ika Parma Dewi
Keyword(s):  
Learning Outcomes ◽  
Alternative Hypothesis ◽  
Hypothesis Test ◽  
Digital Simulation ◽  
Sampling Technique ◽  
Learning Model ◽  
Homogeneity Test ◽  
Model Learning ◽  
Direct Learning ◽  
The Difference

This research aimed to analyze the difference of learning outcomes by using Student Facilitator and Explaining model and direct learning model on Digital Simulation subject at class X TKJ SMKN 1 Tilatang Kamang. The problem in this study was the student learning result which were still under KKM on Digital Simulation subjects at SMKN 1 Tilatang Kamang. Type of this research was Quasi Experiment. The sample was taken through Probability Sampling technique. The research samples were class X TKJ A  and class X TKJ B. Class X TKJ A as sample for experiment class using Student Facilitator and Explaining  model and class X TKJ B as sample for control class using direct learning model. Data analyzed based on post-test experiment class and control class, then analyzed for normality test, homogeneity test and hypothesis test. From the experimental class, the research results obtained an average of 82.47, while the control class was averaged of 76.94. Result of hypothesis calculation at significant level α = 0,05 found tcount> ttable that is 1,78> 1,699, because tcount was bigger than ttable, so null hypothesis (H0) was rejected and alternative hypothesis (Ha) was accepted. It can be concluded at the real level that this study showed that Student Facilitator and Explainingmodel gave significant effect on students learning results of Digital Simulation at class X TKJ in SMKN 1 Tilatang Kamang. Therefore, the Student Facilitator and Explaining model is better than the direct learning model.Keywords: Student Facilitator and Explaining Model,Direct Learning Model, Learning outcomes, Experiment Class, Control Class.

scholarly journals PERBANDINGAN HASIL BELAJAR KIMIA SISWA MENGGUNAKAN MODEL PEMBELAJARAN WORD SQUARE DAN MODEL PEMBELAJARAN SCRAMBLE DI MAN 2 KOTA BENGKULU

Alotrop ◽  
2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Weny Shintia ◽  
Amrul Bahar ◽  
Rina Elvia
Keyword(s):  
Data Analysis ◽  
Student Learning ◽  
Learning Outcomes ◽  
Hypothesis Test ◽  
Student Learning Outcomes ◽  
T Test ◽  
Homogeneity Test ◽  
Model Learning ◽  
Average Value ◽  
Normality Test

This study aimed to compare the chemistry learning outcomes of students with using word square model learning and scramble model learning in grade X MAN 2 Kota Bengkulu on the subject of chemistry compound nomenclature. This was quasy experimental research and held in March to May 2018. Population in this study is the entire class X MIA in MAN 2 Kota Bengkulu  2017/2018  academic   year,  amounting  to  149  students.  Sample  is  class  X  MIA  1  and  class  X  MIA  3.        The sample of the research is class X MIA 1 with 32 students and class X MIA 3 with 33 students. Data analysis used normality test, homogeneity test and hypothesis test (t test). Data analysis was performed using Statistical Package for The Social Science (SPSS) version 16.Improvement student’s learning outcomes in this research was taken from difference assess of pretest and posttest. At experiment class of I average value improvement  of student’s learning outcomes was 50.32. while at experiment  class of II, average value improvement of student’s learning outcomes was 44.4 . through some statistic test, there was t-test (? = 0.05) which done test the hypothesis to obtained the test result was t value = 2.174 and t tabel = 1.998. The result of data analysis showed that were significant differences in student learning outcomes between the class which implemented word square model learning and the class which implemented scramble model learning. Student learning outcomes that apply the word square model of learning better than student learning outcomes that apply the scramble model of learning.

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