Effect Of Learning Models Information Search On Results Learn Civic Education Students Class Sevent At Junior High School Public Three Koto Tuo XIII Sub Districts, Koto Kampar Dstricts

The observation author in class seventh at Junior High School PublicThree Koto Tuo XIII Sub Districts, Koto Kampar Districts encountered facts orphenomena especially on the lessons Civic Education that of 36 people studentclass seventh at Junior High School Public Three Koto Tuo XIII Sub Districts,Koto Kampar Districts only the results learn obtained students not optimal, it isseen from the test result, students results learn is still at an average grade class56,67 or still below the value KKM which has been established that 70. Modelsinformation search constitute one way that teachers do it turn on subject matterare considered dry. Students seeking the material in groups and answer theteacher\u27s question to them. (Hisyam Zaini, 2008:48). The research wasconducted in the classroom seventh at Junior High School Public Three Koto TuoXIII Sub Districts, Koto Kampar Districts. The population in this research wereall students class seventh Junior High School Public Three Koto Tuo XIII SubDistricts, Koto Kampar Districts which amounts to 97 people student. The samplein research taken by Purposive sampling. Class of the sample is class VIIb whichamounts to 32 student\u27s and class VIIc which amount to 32 student\u27s. So overallis 64 student\u27s.Based formulation of the problem, in concluded that there is influenceresult learn Civic Education are tough to learning models Information Searchstudent\u27s class seventh Junior High School Public Three Koto Tuo XIII SubDistricts, Koto Kampar Districts. Based hypothesis testing value ‫ݐ‬௛௜௧௨௡௚ which isgreater than ‫ݐ‬௧௔௕௘௟ at significant level 5% an 1% (5.128 > 1.671) means oninfluence Result Learn Civic Education are tough use learning methodsInformation Search with learning Methods on student class VII Junior HighSchool Public Three Koto Tuo XIII Sub Districts, Koto Kampar Districts. That isto say, that the more often the better implementation are tough to use learningmodels Information Search the better and also high results learn Student\u27s inClass VII Junior High School Public Three Koto Tuo XIII Sub Districts, KotoKampar District

Nature-Inspired Learning Models

Intelligent learning mechanisms found in natural world are still unsurpassed in their learning performance and eficiency of dealing with uncertain information coming in a variety of forms, yet remain under continuous challenge from human driven artificial intelligence methods. This work intends to demonstrate how the phenomena observed in physical world can be directly used to guide artificial learning models. An inspiration for the new learning methods has been found in the mechanics of physical fields found in both micro and macro scale. Exploiting the analogies between data and particles subjected to gravity, electrostatic and gas particle fields, new algorithms have been developed and applied to classification and clustering while the properties of the field further reused in regression and visualisation of classification and classifier fusion. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some testing over the well-known real and artificial datasets, compared when possible to the traditional methods

Understanding from Machine Learning Models

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding

Estimating Learning Models with Experimental Data

We study the statistical properties of three estimation methods for a model of learning that is often tted to experimental data: quadratic deviation measures without unobserved heterogeneity, and maximum likelihood with and without unobserved heterogeneity. After discussing identi cation issues, we show that the estimators are consistent and provide their asymptotic distribution. Using Monte Carlo simulations, we show that ignoring unobserved heterogeneity can lead to seriously biased estimations in samples which have the typical length of actual experiments. Better small sample properties are obtained if unobserved heterogeneity is introduced. That is, rather than estimating the parameters for each individual, the individual parameters are considered random variables, and the distribution of those random variables is estimated

The Feedback on Learning Models Making Material Fashion Pattern

The purpose of this study is to find out the effect of the feedback and learning models toward the students' achievement on dress pattern making by controlling the students' artistic talent of class X of the State Vocation Schools in Denpasar Bali. This research is 2 x 2 Experimental Design Factorial. The samples were taken based on the Multistage Random Sampling with 80 students. This study applied the analysis of covariance (ANCOVA). The results, after controlling the students' artistic talent, are as follow: (1) the achievement in dress pattern making for the students who were given the feedback of the formative test immediately after the test given is higher than those who were given the formative test feedback delayed, (2) the achievement in dress pattern making for the students who were given the cooperative learning model is higher than those who were given the conventional learning model, (3) there was an effect on the interaction between the formative test feedback and the learning models towards the students' achievement in dress pattern making, (4) the students who were given the formative test feedback immediately after the test given and were taught by using CO-Op cooperative learning model is higher than those who were taught by using conventional teaching model, (5) the students who were given the formative test feedback delayed after the test given and were taught by using CO-Op Cooperative Learning Model is lower than those who were taught by using conventional teaching model, (6) the students who were taught by using the Co-Op cooperative learning model and were given the formative test feedback immediately after the test given show higher achievement in the dress pattern making than those who were given the formative test feedback delayed, and (7) the students who were taught by using the conventional learning model and were given the feedback immediately after the test given show lower achievement in the dress pattern making than those who were given the feedback delayed after controlling the artistic talent. Based on the research findings, it is recommended to the teachers of the state vocational school that: to improve the students achievement in dress pattern making, the teachers need to use the formative tests feedback and learning models correctly and carefully

Machine Learning Models that Remember Too Much

Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data