Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Machine learning.”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,729 records · Page 96Linked to original sources
PubMed2006

Developmental learning with behavioral mode tuning by carrier-frequency modulation in coherent neural networks.

We propose a developmental learning architecture with which a motion-control system learns multiple tasks similar to each other or advanced ones incrementally and efficiently by tuning its behavioral mode. The system is based on a coherent neural network whose carrier frequency works as a mode-tuning parameter. In our experiments, we consider two tasks related to bicycle riding. The first is to ride as temporally long as the system can before it falls down (task 1). The second is an advanced one, i.e., to ride as far as possible in a certain direction (task 2). We compare developmental learning to learn task 2 after task 1 with the direct learning of task 2. We also examine the effect of the mode tuning by comparing variable-mode learning (VML), where the carrier frequency is set free to move, with fixed-mode learning (FML), where the frequency is unchanged. We find that VML developmental learning results in the most efficient learning among the possible combinations. We discuss the effects of the incremental task assignment as well as the behavioral mode tuning in developmental learning.

Adaptation, Physiological↗
PubMed2005

An improved cluster labeling method for support vector clustering.

The support vector clustering (SVC) algorithm is a recently emerged unsupervised learning method inspired by support vector machines. One key step involved in the SVC algorithm is the cluster assignment of each data point. A new cluster labeling method for SVC is developed based on some invariant topological properties of a trained kernel radius function. Benchmark results show that the proposed method outperforms previously reported labeling techniques.

Algorithms↗
PubMed2005

Learning bounds for kernel regression using effective data dimensionality.

Kernel methods can embed finite-dimensional data into infinite-dimensional feature spaces. In spite of the large underlying feature dimensionality, kernel methods can achieve good generalization ability. This observation is often wrongly interpreted, and it has been used to argue that kernel learning can magically avoid the "curse-of-dimensionality" phenomenon encountered in statistical estimation problems. This letter shows that although using kernel representation, one can embed data into an infinite-dimensional feature space; the effective dimensionality of this embedding, which determines the learning complexity of the underlying kernel machine, is usually small. In particular, we introduce an algebraic definition of a scale-sensitive effective dimension associated with a kernel representation. Based on this quantity, we derive upper bounds on the generalization performance of some kernel regression methods. Moreover, we show that the resulting convergent rates are optimal under various circumstances.

Artificial Intelligence↗
PubMed1989

Effect of uncertainty and diagnosticity on classification of multidimensional data with integral and separable displays of system status.

Integrative, objectlike displays have been advocated for presenting multidimensional system data. In this research two experiments assess the effect of uncertainty on the processing of integral and separable displays. In each experiment 30 subjects were trained to classify instances of system state into one of four state categories using a configural display, a bar graph display, or a digital display. In Experiment 1 the range of instances from the state categories was uniform; in Experiment 2 the distribution was biased toward those instances of highly uncertain state category membership. After training, subjects received extended practice classifying system data. In both experiments uncertainty was found to have the greatest effect on classification performance. In Experiment 1 the bar graph display was consistently superior; the configural display was superior to the digital display only under conditions of low uncertainty. In Experiment 2 the superiority of the bar graph display diminished, producing results equivalent to those of the digital display, with the configural display producing the worst performance. The effect of uncertainty on classification performance is discussed, with specific attention paid to the apparent configural and separable properties of the bar graph display.

Adolescent↗
PubMed1998

It is not how much you have but how you use it: toward a rational use of simulation to support aviation training.

One of the most remarkable changes in aviation training over the past few decades is the use of simulation. The capabilities now offered by simulation have created unlimited opportunities for aviation training. In fact, aviation training is now more realistic, safe, cost-effective, and flexible than ever before. However, we believe that a number of misconceptions--or invalid assumptions--exist in the simulation community that prevent us from fully exploiting and utilizing recent scientific advances in a number of related fields in order to further enhance aviation training. These assumptions relate to the overreliance on high-fidelity simulation and to the misuse of simulation to enhance learning of complex skills. The purpose of this article is to discuss these assumptions in the hope of initiating a dialogue between behavioral scientists and engineers.

Aviation↗
PubMed2004

Learning-induced improvement in encoding and decoding of specific movement directions by neurons in the primary motor cortex.

Many recent studies describe learning-related changes in sensory and motor areas, but few have directly probed for improvement in neuronal coding after learning. We used information theory to analyze single-cell activity from the primary motor cortex of monkeys, before and after learning a local rotational visuomotor task. We show that after learning, neurons in the primary motor cortex conveyed more information about the direction of movement and did so with relation to their directional sensitivity. Similar to recent findings in sensory systems, this specific improvement in encoding is correlated with an increase in the slope of the neurons' tuning curve. We further demonstrate that the improved information after learning enables a more accurate reconstruction of movement direction from neuronal populations. Our results suggest that similar mechanisms govern learning in sensory and motor areas and provide further evidence for a tight relationship between the locality of learning and the properties of neurons; namely, cells only show plasticity if their preferred direction is near the training one. The results also suggest that simple learning tasks can enhance the performance of brain-machine interfaces.

Animals↗
PubMed1983

Micromatrix. Apple II.

Microcomputer sales are netting millions of dollars as consumers take a serious interest in computer software and hardware. Hospital professionals are fast learning the value of these multicapable machines and their programs for their management needs. Because of this growing trend, and the need to keep up with the latest developments in computer technology, Health Matrix initiates this forum. Each issue will feature an analysis of various microcomputers for the experienced and novice computer user.

Computers↗
PubMed1975

Reinforcement of cooperation between profoundly retarded adults.

An experimental approach to the development and maintenance of cooperation responses in profoundly retarded institutionalized male adults was evaluated in this study. A single-subject reversal design was used for the major experiment which involved automatic recording of cooperative responses. Within a relatively short period, 7 dyads learned independent operation of the cooperation machine. During the first reinforcement period, a high and fairly stable rate of cooperative responding occurred which decreased markedly after several extinction sessions and immediately recovered when reinforcements were reinstituted. The operation of such machines by profoundly retarded subjects resulted in increased social interaction beyond purely mechanistic behavior.

Adult↗
PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗
PubMed2001

Simple recurrent networks learn context-free and context-sensitive languages by counting.

It has been shown that if a recurrent neural network (RNN) learns to process a regular language, one can extract a finite-state machine (FSM) by treating regions of phase-space as FSM states. However, it has also been shown that one can construct an RNN to implement Turing machines by using RNN dynamics as counters. But how does a network learn languages that require counting? Rodriguez, Wiles, and Elman (1999) showed that a simple recurrent network (SRN) can learn to process a simple context-free language (CFL) by counting up and down. This article extends that to show a range of language tasks in which an SRN develops solutions that not only count but also copy and store counting information. In one case, the network stores information like an explicit storage mechanism. In other cases, the network stores information more indirectly in trajectories that are sensitive to slight displacements that depend on context. In this sense, an SRN can learn analog computation as a set of interdependent counters. This demonstrates how SRNs may be an alternative psychological model of language or sequence processing.

Models, Neurological↗
PubMedDate not supplied

Interpretation of symptoms with a data-processing machine. 1959.

Recognizing that machines in the practice of medicine are here to stay, physicians have the obligation to learn as much of their advantages and limitations as they can comprehend. The machine described here merely correlates symptoms set down by the patient and draws conclusions on the basis of what it has "learned" from physicians. Hence it makes the same errors as the human brain which "taught" it plus others that are inherent in its inability to initiate the thinking process. One reviewer of the paper presented below asked this important question, "What is the character of the error when a diagnosis is made which is not correct? If a patient with flat feet is simply not so diagnosed, this is one type of error, but if the machine reads, ¿respiratory tuberculosis inactive,' it's another." This and many other questions properly may arise. At the same time, the device is an extremely ingenious one and does eliminate the factors of emotional bias and fatigue, which may confuse the issue when a physician tries to analyze the complex of subjective complaints which the patient serves up to him. Whether the machine described here will have a place in the internists' armamentarium cannot be determined until more of them know about it.

Computers↗
PubMed2006

Learning interpretable SVMs for biological sequence classification.

BACKGROUND: Support Vector Machines (SVMs)--using a variety of string kernels--have been successfully applied to biological sequence classification problems. While SVMs achieve high classification accuracy they lack interpretability. In many applications, it does not suffice that an algorithm just detects a biological signal in the sequence, but it should also provide means to interpret its solution in order to gain biological insight. RESULTS: We propose novel and efficient algorithms for solving the so-called Support Vector Multiple Kernel Learning problem. The developed techniques can be used to understand the obtained support vector decision function in order to extract biologically relevant knowledge about the sequence analysis problem at hand. We apply the proposed methods to the task of acceptor splice site prediction and to the problem of recognizing alternatively spliced exons. Our algorithms compute sparse weightings of substring locations, highlighting which parts of the sequence are important for discrimination. CONCLUSION: The proposed method is able to deal with thousands of examples while combining hundreds of kernels within reasonable time, and reliably identifies a few statistically significant positions.

Algorithms↗
PubMed1997

Predicting the toxicity of complex mixtures using artificial neural networks.

Industrial and municipal wastewaters constitute major sources of contamination of the aquatic compartment and represent a threat to aquatic life. Artificial neural networks based on three different learning paradigms were studied as a means of predicting acute toxicity to trout (5 days exposure to wastewaters) using input data from two simple microbiotests requiring only 5 or 15 min of incubation. These microbiotests were 1) the chemoluminescent peroxidase (Cl-Per) assay, which can detect radical scavengers and enzyme-inhibiting substances, and 2) the luminescent bacteria toxicity test (Microtox), in which reduction of light emission by bacteria during exposure is taken as a measure of toxicity. The responses obtained with the trout bioassay, the Cl-Per and the Microtox test were analyzed through statistical correlation (Pearson product-moment correlation), unsupervised learning by a self-organizing network, and assisted learning by the backpropagation and the Boltzmann machine (probabilistic) paradigms. No significant correlation (p < 0.05) was found between the responses obtained with either the Cl-Per assay (p = 0.121) or the Microtox (p = 0.061) microbiotest and those resulting from the trout bioassay. The self-organizing network was able to identify by itself a maximum of five classes that were more or less relevant for predicting toxicity to fish: class 1 contained 2 samples that were toxic to fish, class 2 contained 2/3 samples that were toxic, class 3 showed 6/8 samples that were non toxic, class 4 contained 5/6 samples that were non-toxic and class 5 comprised one sample that was toxic. Supervised learning with backpropagation analysis yielded two kinds of networks that hold potential. The first one was able to predict the actual toxic wastewater concentration with an overall performance of 65% when fed fresh data, while the second one, which was designed to differentiate between toxic and non-toxic effluents, exhibited a much better performance (90%). However, the probabilistic network also proved to be a very good predictive model for toxicity to fish, with an overall performance of 90%. Although more data are needed, the network based on the backpropagation paradigm seems to be a better predictor or classifier of trout toxicity when used with the Cl-Per and the Microtox microbiotests.

Algorithms↗
PubMed1993

Physiological deactivation after two contrasting tasks at a video display terminal: learning vs repetitive data entry .

Two contrasting 90 min VDT work situations were simulated in the laboratory: (1) a machine-paced, repetitive data entry task; and (2) a stimulating, self-paced learning task with successive feedback. Thirty non-smoking male students (20-34 years), without previous experience of VDT work, participated individually in each condition on two consecutive days (balanced order) and in a task-free baseline condition. Self-reports and successive measurements (ambulatory recordings) of systolic and diastolic blood pressure and heart rate were obtained during work and during a subsequent 60 min period of deactivation. Urine samples were obtained after each period for the determination of catecholamines and cortisol. In the baseline condition, measurements were obtained at corresponding times of the day. As expected, the data entry task was associated with self-reports of boredom, irritation, and unpleasantness; the learning task wtih alertness, interest, and ability to concentrate. Similar elevations of physiological measurements occurred in both work situations. However, differences between conditions were found after work. Following data entry, deactivation was slower in five of the six variables (significant for epinephrine).

Adult↗