Learning problems

This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place. We achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map,
and is particularly suitable for online loop closure detection in mobile robotics.

Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtained in several areas, mostly on vision and language data sets. The best results obtained on supervised learning tasks involve an unsupervised learning component, usually in an unsupervised pre-training phase. Even though these new algorithms

With the aim of studying the formation of brain mechanisms of voluntary activity regulation in junior schoolchildren and the role of these mechanisms in the acquisition of the skill of writing, a complex neuropsychological investigation was carried out. In the EEG of children with learning problems and a deficit of voluntary activity regulation (VAR), signs of the functional immaturity of

Students are taught a large variety of life structures and processes at the cellular level. The concepts used to describe them are mainly drawn from the sub-cellular level, but this knowledge seems to be fragmentary if its integration at the cellular and organismic level remains undone. As a consequence, many students fail to acquire coherent conceptual understanding of the cell

In the field of chemistry there has been considerable interest in discovering the reasons why many students have great difficulty in successfully developing a scientifically accepted understanding of chemical equilibrium. Research projects influenced and inspired by constructivism have been invaluable for identifying alternative conceptions that may turn into obstacles to meaningful learning of fundamental ideas regarding chemical equilibrium. Research findings

ABSTRAK Sekarang ini merupakan era dimana teknologi memegang peranan penting dalam dunia Pendidikan. Teknologi berkembang pesat dengan adanya arus tak terbatas globalisasi dunia. Teknologi dan Pendidikan sekarang ini menjadi sebuah trend yang mana dengan adanya teknologi canggih membuat Pendidikan semakin mudah diakses dan dijangkau setiap kalangan. Adanya sistem daring membuat teknologi-teknologi yang ada menjadi berkembang, hal ini tentu dapat memudahkan siswa maupun guru. Khusus untuk guru, dengan kemajuan teknologi membuat mereka semakin mudah dalam mendapatkan informasi dan berinovasi dalam membuat kegiatan dalam pembelajaran di kelas. E-learning atau Pembelajaran daring menggunakan koneksi internet adalah yang paling sering digunakan saat ini. Dalam dunia Pendidikan, kita mengenal Pendidikan inkulsi yang mana ini merupakan layanan Pendidikan untuk anak yang memilki kebutuhan khusus. Selama ini, Pendidikan inklusi hanya dilakukan layakany seperti pada biasanya, seperti guru memberikan perhatian yang lebih terhadap siswa berkebutuhan untuk membantu mereka belajar. Pendidikan inklusi dan penggunaan e-learning merupakan hal yang jarang ditemui untuk saat ini, namun ada beberapa Lembaga Pendidikan yang telah menggunakannya. Meskipun bisa menggunakanna tetap saja dalam pelaksanaannya masih ada kendala. Maka dari itu hal ini penting untuk dikaji dimana dengan penggunaan teknologi berupa e-learning sebagai sarana dalam menjalankan Pendidikan inklusi membantu proses belajar untuk siswa berkebebutuhan khsusus di era saat ini. Kemudian, penelitian kali ini akan membahas permasalahan yang terjadi terhadap penggunakaan teknologi dan e-learning terhadap Pendidikan inklusi. Penelitian ini menggunakan metode studi kepustakaan. Untuk memperoleh data yang diperlukan, peneliti menggunakan dokumentasi dan studi literatur. Dalam artikel ini akan menguraikan permasalahan yang terjadi pada penggunaan e-learning

3 rd International Conference on Big Data and Machine Learning (BDML 2022) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning.

4th International Conference on Machine Learning & Applications (CMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

Scope & Topics 3 rd International Conference on Artificial Intelligence and Machine Learning (CAIML 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and Machine Learning. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Machine Learning in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

3rd International Conference on Advanced Machine Learning (AMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning Techniques and Data Science.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

3 rd International Conference on Big Data and Machine Learning (BDML 2022) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning.

4th International Conference on Machine Learning & Applications (CMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

4
th International Conference on Machine Learning & Applications (CMLA 2022) will
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of on Machine Learning & Applications. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the following areas, but are not limited to.

Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer- reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.

Scope & Topics 4 th International Conference on Machine Learning & Applications (CMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

4
th International Conference on Machine Learning & Applications (CMLA 2022) will
provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of on Machine Learning & Applications. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.

4th International Conference on Machine Learning & Applications (CMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Learning a large number of simple local concepts is both faster and easier than learning a single global concept. Inspired by this principle of divide and conquer, a number of modular learning approaches have been proposed by the computational intelligence community. In modular learning, the classification/regression/clustering problem is first decomposed into a number of simpler subproblems, a module is learned for each of these subproblems, and finally their results are integrated by a suitable combining method. Mixtures of experts and clustering are two of the techniques that are describable in this paradigm. In this paper we present a broad framework for Generalized Associative Modular Learning Systems (GAMLS). Modularity is introduced through soft association of each training pattern with every module. The coupled problems of learning the module parameters and learning associations are solved iteratively using deterministic annealing. Starting at a high temperature with only one module, GAMLS framework automatically evolves the required number of modules through a systematic growing and pruning technique. Each phase begins by splitting every module in the previous phase into two, updating these new modules and then pruning and merging any redundant modules. A phase transition is induced by temperature decay. A number of existing modular learning problems, both unsupervised (clustering, mixture model density, mixture of principal components) and supervised (mixture of experts, radial basis function networks), can be effectively tackled in GAMLS. Case studies for clustering and regression using mixture of experts are provided for a number of datasets showing the efficacy of the GAMLS framework in evolving the right number of modules, inducing interpretable localizations among modules and robustness of the solution obtained. More importantly, this framework provides a unifying view for understanding and characterizing modular learning methods.

Scope & Topics 4 th International Conference on Machine Learning & Applications (CMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

4
th International Conference on Machine Learning & Applications (CMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

3 rd International Conference on Big Data and Machine Learning (BDML 2022) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning.