Incremental Learning
Incremental Learning - Related Videos
01:24
The Incremental Cash Flows
0 Views •
2026
Incremental cash flows are a critical consideration for evaluating leasing versus purchasing decisions. These include lease payments, potential tax benefits, and cost savings relative to buying. Analyzing these cash flows provides valuable insights into the financial implications of leasing and helps determine its suitability for a business.Leasing involves recurring payments that are compared against the costs and benefits of ownership, such as maintenance expenses and tax advantages like...
07:05
A New Workflow for Sampling and Digitizing Increment Cores
0 Views •
Cited by 11 •
2024
We present a protocol to use 3D-printed mounts to fix increment cores in the field without the necessity of unpacking and gluing them on wooden mounts. The new GSC holder allows placing the cores in a core microtome to cut their surface and directly transfer them to digital image capturing.
10:06
An Introduction to Learning and Memory
0 Views •
2023
Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as non-associative and associative learning, and different types of memory, such as long-term and short-term memory, have been associated with human behaviors. Studying these components in detail helps behavioral scientists understand the neural mechanisms behind these two complex phenomena. JoVE's overview on learning and memory introduces common...
06:02
Visual Statistical Learning
0 Views •
2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The name...
07:40
Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices
0 Views •
Cited by 5 •
2023
Here, we present a protocol on how to avoid micro cracks in increment cores by applying a cordless drill with a torque multiplier to minimize problems when coring trees, as well as its effect on preparing long micro sections. This protocol also includes a procedure to sharpen corers in the field.
FAQs
Catastrophic forgetting occurs when adapting to newer examples causes a model to lose useful patterns learned earlier. Incremental learning can limit this problem by replaying selected stored samples or by constraining how much existing parameters change during an update. These safeguards help preserve earlier predictive behavior while incorporating new information, which is important when clinical data arrive over time.
Data quality, ongoing monitoring, and validation strongly influence whether an incremental update improves a model or introduces errors. New clinical records, images, or observations may not represent earlier data consistently, so each update needs evaluation against retained knowledge and current performance. This makes adaptation a controlled process rather than an automatic guarantee of better predictions.
When new information is incorporated, the model may reflect biases present in that information, especially if data quality or coverage changes over time. Bias protection therefore requires monitoring and validation during updates, not only before deployment. This is particularly relevant when disease patterns, clinical practices, or patient records evolve and alter the information available to the system.
A practical workflow begins with obtaining new patient records, imaging data, or other relevant observations, then updating model parameters while retaining useful earlier information. The updated system is monitored and validated to assess predictive behavior, data quality, and possible bias. Repeating these checks as new batches arrive helps determine whether continued adaptation is justified.
Medical applications can draw on evolving patient records, imaging data, clinical practices, and disease patterns. These sources represent different forms of change that a model may need to accommodate over time. Selecting relevant incoming data and checking their quality helps ensure that updates support the intended clinical use rather than simply reflecting every new observation.
Incremental learning can support decision-support systems that need to remain responsive as clinical information changes. It can also contribute to personalized risk prediction by incorporating newer information about an individual or a changing disease context. In both cases, usefulness depends on monitoring, validation, data quality, and attention to bias during ongoing updates.