1,929,662 research outputs found
The Enhancement of Mathematical Communication and Self Regulated Learning of Senior High School Students Through PQ4R Strategy Accompanied by Refutation Text Reading
This study is experiment research with control group pretest-posttest design and aimed to examine the influence of PQ4R strategy and Refutation Text, school level, and student’s mathematical early knowledge toward achievement and enhancement of student’s mathematical communication ability and Self Regulated Learning. Subject of study as much as 241 students of class X from three Public Senior High School from high, medium, and low school level. Research instrument consist of one set of student’s mathematical communication, and one set of student’s Self Regulated Learning scale. Data analysis use Kosmogorov-Smirnov Test (Test-Z), Level Test, Test-t, one-way and two-way ANOVA, Post Hoc Test (Scheffe) and also Chi-Square Test. Study found that learning with PR4R strategy accompanied by Refutation Text Reading give consistent influence compared with conventional learning as viewed as a whole, based on school level and also mathematical early knowledge. In addition, study also found: (1) there is no interaction between learning (PQ4R) accompanied by Refutation Text reading and conventional and school level toward (a) student’s mathematical communication and (b) student’s Self Regulated Learning; (2) there is no significant interaction between learning and student’s mathematical early knowledge toward (a) student’s mathematical communication ability and (b) student’s Self Regulated Learning; and (3) there is association between student’s mathematical communication ability and student’s Self Regulated Learning.
Keywords: PQ4R, Refutation Text, Mathematical Communication, and Self Regulated Learning
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage
The results from most machine learning experiments are used for a specific
purpose and then discarded. This results in a significant loss of information
and requires rerunning experiments to compare learning algorithms. This also
requires implementation of another algorithm for comparison, that may not
always be correctly implemented. By storing the results from previous
experiments, machine learning algorithms can be compared easily and the
knowledge gained from them can be used to improve their performance. The
purpose of this work is to provide easy access to previous experimental results
for learning and comparison. These stored results are comprehensive -- storing
the prediction for each test instance as well as the learning algorithm,
hyperparameters, and training set that were used. Previous results are
particularly important for meta-learning, which, in a broad sense, is the
process of learning from previous machine learning results such that the
learning process is improved. While other experiment databases do exist, one of
our focuses is on easy access to the data. We provide meta-learning data sets
that are ready to be downloaded for meta-learning experiments. In addition,
queries to the underlying database can be made if specific information is
desired. We also differ from previous experiment databases in that our
databases is designed at the instance level, where an instance is an example in
a data set. We store the predictions of a learning algorithm trained on a
specific training set for each instance in the test set. Data set level
information can then be obtained by aggregating the results from the instances.
The instance level information can be used for many tasks such as determining
the diversity of a classifier or algorithmically determining the optimal subset
of training instances for a learning algorithm.Comment: 7 pages, 1 figure, 6 table
A Winnow-Based Approach to Context-Sensitive Spelling Correction
A large class of machine-learning problems in natural language require the
characterization of linguistic context. Two characteristic properties of such
problems are that their feature space is of very high dimensionality, and their
target concepts refer to only a small subset of the features in the space.
Under such conditions, multiplicative weight-update algorithms such as Winnow
have been shown to have exceptionally good theoretical properties. We present
an algorithm combining variants of Winnow and weighted-majority voting, and
apply it to a problem in the aforementioned class: context-sensitive spelling
correction. This is the task of fixing spelling errors that happen to result in
valid words, such as substituting "to" for "too", "casual" for "causal", etc.
We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a
statistics-based method representing the state of the art for this task. We
find: (1) When run with a full (unpruned) set of features, WinSpell achieves
accuracies significantly higher than BaySpell was able to achieve in either the
pruned or unpruned condition; (2) When compared with other systems in the
literature, WinSpell exhibits the highest performance; (3) The primary reason
that WinSpell outperforms BaySpell is that WinSpell learns a better linear
separator; (4) When run on a test set drawn from a different corpus than the
training set was drawn from, WinSpell is better able than BaySpell to adapt,
using a strategy we will present that combines supervised learning on the
training set with unsupervised learning on the (noisy) test set.Comment: To appear in Machine Learning, Special Issue on Natural Language
Learning, 1999. 25 page
On classifying images using Keras and Tensorflow in Python
This hands-on presentation will be focused on practical, essential aspects that are necessary in order to build a custom classifier. The tutorial will start from prerequisites, like the libraries that are necessary to install, to the step-by-step procedure for classifying new classes, which have not been previously learnt, by a pre-trained model using transfer learning. Such a separation of new classes of objects in images starts with the building of the novel image data set, its separation into training, validation and test sets. The model will learn to distinguish the objects from the images in the training set, it will be tuned on a validation set and finally it will face images from the previously unseen test set.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tec
Learning programs by learning from failures
We describe an inductive logic programming (ILP) approach called learning
from failures. In this approach, an ILP system (the learner) decomposes the
learning problem into three separate stages: generate, test, and constrain. In
the generate stage, the learner generates a hypothesis (a logic program) that
satisfies a set of hypothesis constraints (constraints on the syntactic form of
hypotheses). In the test stage, the learner tests the hypothesis against
training examples. A hypothesis fails when it does not entail all the positive
examples or entails a negative example. If a hypothesis fails, then, in the
constrain stage, the learner learns constraints from the failed hypothesis to
prune the hypothesis space, i.e. to constrain subsequent hypothesis generation.
For instance, if a hypothesis is too general (entails a negative example), the
constraints prune generalisations of the hypothesis. If a hypothesis is too
specific (does not entail all the positive examples), the constraints prune
specialisations of the hypothesis. This loop repeats until either (i) the
learner finds a hypothesis that entails all the positive and none of the
negative examples, or (ii) there are no more hypotheses to test. We introduce
Popper, an ILP system that implements this approach by combining answer set
programming and Prolog. Popper supports infinite problem domains, reasoning
about lists and numbers, learning textually minimal programs, and learning
recursive programs. Our experimental results on three domains (toy game
problems, robot strategies, and list transformations) show that (i) constraints
drastically improve learning performance, and (ii) Popper can outperform
existing ILP systems, both in terms of predictive accuracies and learning
times.Comment: Accepted for the machine learning journa
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Smart mobility management would be an important prerequisite for future fog
computing systems. In this research, we propose a learning-based handover
optimization for the Internet of Vehicles that would assist the smooth
transition of device connections and offloaded tasks between fog nodes. To
accomplish this, we make use of machine learning algorithms to learn from
vehicle interactions with fog nodes. Our approach uses a three-layer
feed-forward neural network to predict the correct fog node at a given location
and time with 99.2 % accuracy on a test set. We also implement a dual stacked
recurrent neural network (RNN) with long short-term memory (LSTM) cells capable
of learning the latency, or cost, associated with these service requests. We
create a simulation in JAMScript using a dataset of real-world vehicle
movements to create a dataset to train these networks. We further propose the
use of this predictive system in a smarter request routing mechanism to
minimize the service interruption during handovers between fog nodes and to
anticipate areas of low coverage through a series of experiments and test the
models' performance on a test set
Do We Train on Test Data? Purging CIFAR of Near-Duplicates
The CIFAR-10 and CIFAR-100 datasets are two of the most heavily benchmarked
datasets in computer vision and are often used to evaluate novel methods and
model architectures in the field of deep learning. However, we find that 3.3%
and 10% of the images from the test sets of these datasets have duplicates in
the training set. These duplicates are easily recognizable by memorization and
may, hence, bias the comparison of image recognition techniques regarding their
generalization capability. To eliminate this bias, we provide the "fair CIFAR"
(ciFAIR) dataset, where we replaced all duplicates in the test sets with new
images sampled from the same domain. We then re-evaluate the classification
performance of various popular state-of-the-art CNN architectures on these new
test sets to investigate whether recent research has overfitted to memorizing
data instead of learning abstract concepts. We find a significant drop in
classification accuracy of between 9% and 14% relative to the original
performance on the duplicate-free test set. The ciFAIR dataset and pre-trained
models are available at https://cvjena.github.io/cifair/, where we also
maintain a leaderboard.Comment: Journal of Imagin
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