.SVM File Recovery

Have files been deleted due to a user’s error or software error? Have you formatted a disk by accident and need to know how to recover the files? Read our .SVM file recovery guide for Windows, MacOS, Android and IOS in 2023.

SVM File Recovery

What is a .SVM file?

File used by OpenOffice and LibreOffice, which are office productivity suites; stores vector and raster graphics when inserting or copying images between the different programs of the respective suites; similar to the Windows Metafile format used by the .WMF file.

What Are Common Causes of ".SVM" Files Lost or Failure?

There can be several common causes for ".SVM" file loss or failure. Some of them include:

  1. Accidental deletion: Users may accidentally delete ".SVM" files while trying to remove unnecessary files or folders.
  2. Software or hardware issues: Issues with the operating system, software conflicts, or hardware malfunctions can lead to file corruption or loss.
  3. Virus or malware attacks: Malicious software can infect and damage ".SVM" files, making them inaccessible or causing them to be deleted.
  4. Power failures: Abrupt power outages or system shutdowns can interrupt file saving processes, leading to file corruption or loss.
  5. Formatting or partitioning errors: Incorrect formatting or partitioning of storage devices can result in the loss of ".SVM" files.
  6. File system errors: File system errors, such as file system corruption or bad sectors on the storage device, can cause ".SVM" file failure or loss.
  7. Human error: Mistakes made by users, such as accidental formatting of storage devices or improper handling of files, can lead to ".SVM" file loss.
  8. Software bugs or glitches: Bugs or glitches in software applications that handle ".SVM" files can cause data corruption or loss.
  9. Physical damage to storage devices: Physical damage to hard drives, USB drives, or other storage devices can result in the loss of ".SVM" files.
  10. Synchronization or backup failures: Failures during the synchronization or backup process can lead to ".SVM" file loss if the files were not properly backed up.

It is always recommended to regularly backup important files and use reliable antivirus software to minimize the risk of ".SVM" file loss or failure.

How to recover lost ".SVM" files?

Sometimes while working with a computer, laptop or other stationary or mobile devices, you may encounter various bugs, freezes, hardware or software failures, even in spite of regular updates and junk cleaning. As a result, an important ".SVM" file may be deleted.

Go to view
🧺 How to Recover Files and Folders After Sending Them to the Recycle Bin and Deleting? (Windows 11)

🧺 How to Recover Files and Folders After Sending Them to the Recycle Bin and Deleting? (Windows 11)

By no means should you think that the only way to recover a ".SVM" file is always to create it once more.

Use programs for recovering ".SVM" files if a file was lost after accidental or deliberate deleting, formatting the memory card or the internal storage, cleaning the storage device, after a virus attack or a system failure.

Programs to recover ".SVM" files

Looking for a way to get files back? In cases when files were deleted and they cannot be restored by using standard operating system tools, use Hetman Partition Recovery.

The tool recovers data from any devices, regardless of the cause of data loss.

Follow the directions below:

  1. Download Hetman Partition Recovery, install and start the program.

  2. The program will automatically scan the computer and display all hard disks and removable drives connected to it, as well as physical and local disks.

    File Recovery Software
  3. Double-click on the disk from which you need to recover ".SVM" files, and select analysis type.

    Hetman Partition Recovery - Analysis Type
  4. When the scanning is over, you will be shown the files for recovery.

    Hetman Partition Recovery - Files that Can be Restored
  5. To find a file you need, use the program’s interface to open the folder it was deleted from, or go to the folder "Content-Aware Analysis" and select the required file type.

    Hetman Partition Recovery - Deep Scan
  6. Select the files you have been looking for and click "Recovery".

    File Recovery Software - Files List for Recovery
  7. Choose one of the methods for saving the files and recover them.

    Saving recovered files in Hetman Partition Recovery

How to open file with ".SVM" extension?

Looking for how to open a stereo starView Metafile image file file?

Programs that open ".SVM" files

Apache OpenOffice Apache OpenOffice
LibreOffice LibreOffice
Apache OpenOffice Apache OpenOffice
LibreOffice LibreOffice
Apache OpenOffice Apache OpenOffice
LibreOffice LibreOffice

Additional Information

  • File type: StarView Metafile

  • File extension: .SVM

  • Developer: StarDivision

  • Category: Raster Image Files

  • Format: N/A


We will be happy to answer your questions!

Comments (1)

  • Hetman Software: Data Recovery
    Hetman Software: Data Recovery 18.12.2019 14:04 #
    Leave a comment if you have any questions about Recovering lost .SVM files after deleting, cleaning or formatting!
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Vladimir Artiukh

Author: Vladimir Artiukh, Technical Writer

Vladimir Artiukh is a technical writer for Hetman Software, as well as the voice and face of their English-speaking YouTube channel, Hetman Software: Data Recovery for Windows. He handles tutorials, how-tos, and detailed reviews on how the company’s tools work with all kinds of data storage devices.

Oleg Afonin

Editor: Oleg Afonin, Technical Writer

Oleg Afonin is an expert in mobile forensics, data recovery and computer systems. He often attends large data security conferences, and writes several blogs for such resources as xaker.ru, Elcomsoft and Habr. In addition to his online activities, Oleg’s articles are also published in professional magazines. Also, Oleg Afonin is the co-author of a well-known book, Mobile Forensics - Advanced Investigative Strategies.


Questions and answers

  • What is the process of SVM recovery and how does it work?

    Support Vector Machines (SVM) are supervised learning models used for classification and regression analysis. The process of SVM recovery refers to the steps involved in training an SVM model, finding the optimal hyperplane, and recovering the support vectors.

    1. Data Preparation: The first step is to prepare the training data, which includes selecting relevant features and labels for each instance.
    2. Model Training: SVM training involves finding the optimal hyperplane that separates the different classes. The algorithm aims to maximize the margin between the hyperplane and the support vectors. This process involves solving a constrained optimization problem.
    3. Kernel Selection: SVMs can utilize different kernel functions to transform the input data into a higher-dimensional feature space. The choice of kernel depends on the problem at hand and can greatly impact the performance of the SVM.
    4. Hyperparameter Tuning: SVMs have hyperparameters that need to be tuned to achieve the best performance. These include the regularization parameter (C) that controls the trade-off between margin maximization and misclassification penalty, as well as the kernel parameters.
    5. Support Vector Identification: Once the SVM model is trained, the support vectors are identified. These are the data points that lie closest to the decision boundary or hyperplane. Support vectors play a crucial role in the classification process.
    6. Model Evaluation: The trained SVM model is evaluated using validation or test data to assess its performance. Various metrics like accuracy, precision, recall, and F1-score can be used to measure the model's effectiveness.

    During the recovery process, the support vectors are used to reconstruct the decision boundary or hyperplane. The support vectors are the critical data points that determine the separation between classes. They have non-zero weights in the SVM model, while other data points have zero weights. By considering only the support vectors, the SVM can efficiently classify new instances without needing to process the entire training dataset.

    Overall, SVM recovery involves training the model, identifying the support vectors, and utilizing them to make predictions efficiently.

  • What are the common reasons for SVM failure and the need for recovery?

    There are several common reasons for SVM (Support Vector Machine) failure and the need for recovery. Some of these reasons include:

    1. Insufficient or poor-quality data: SVM requires a well-labeled and representative dataset for training. If the data is insufficient, noisy, or biased, it can lead to poor model performance and failure. In such cases, data recovery techniques like data augmentation, cleaning, or collecting additional data may be necessary.
    2. Imbalanced classes: When the dataset has imbalanced class distribution, where one class has significantly more instances than the others, SVM may fail to learn the minority class properly. This can result in biased predictions and poor performance. Techniques like undersampling, oversampling, or using class weights can help address this issue.
    3. Overfitting or underfitting: SVM models can suffer from overfitting if they are too complex and capture noise or irrelevant patterns from the training data. On the other hand, underfitting occurs when the model is too simple and fails to capture the underlying patterns. Regularization techniques, adjusting hyperparameters, or using more advanced SVM variants can help recover from these issues.
    4. Non-linearly separable data: SVM is inherently a linear classifier, so it may fail when the data is not linearly separable. In such cases, using non-linear kernels like polynomial or radial basis function (RBF) kernels can help recover the SVM's ability to handle non-linear decision boundaries.
    5. Outliers or anomalies: Outliers in the dataset can significantly affect the SVM's decision boundary, leading to poor generalization. Identifying and handling outliers, either by removing them or using outlier-resistant SVM variants, can help recover from this issue.
    6. Incorrect parameter selection: SVM has various parameters like the choice of kernel, regularization parameter, or kernel-specific parameters. If these parameters are not properly selected or tuned, it can lead to suboptimal performance or failure. Grid search, cross-validation, or other hyperparameter tuning techniques can be used to recover from this situation.

    Overall, understanding the limitations of SVM, diagnosing the specific reason for failure, and applying appropriate recovery techniques can help overcome the common challenges and improve the performance of SVM models.

  • Are there any specific tools or techniques available for SVM recovery, and what are the best practices to ensure successful recovery?

    There are a few tools and techniques available for Support Vector Machine (SVM) recovery. Here are some common ones:

    1. Model checkpointing: During the training process, you can save checkpoints of the model at regular intervals. If the training process is interrupted or fails, you can restart from the last saved checkpoint instead of starting from scratch. This helps in recovering the model and reducing training time.
    2. Early stopping: Implementing early stopping techniques can help prevent overfitting and improve generalization. By monitoring a validation metric (e.g., validation loss or accuracy), you can stop training if the metric does not improve for a certain number of epochs. This prevents wasting time on training a model that is not improving and allows for recovery with the best performing model.
    3. Regularization: Applying regularization techniques such as L1 or L2 regularization can help prevent overfitting and improve the stability of the SVM model. Regularization adds a penalty term to the loss function, which discourages large weights and promotes simpler models.
    4. Cross-validation: Utilizing cross-validation techniques helps evaluate the SVM model's performance on multiple subsets of the data. By splitting the data into several folds and training on different combinations, you can assess the model's generalization ability. If the model fails during training, you can rely on the evaluation results of the completed folds to guide recovery.
    5. Hyperparameter tuning: Properly tuning the hyperparameters of the SVM model can significantly impact its performance and stability. Techniques like grid search or random search can be used to explore different combinations of hyperparameters and find the optimal configuration. By saving the best hyperparameters found during the search, you can ensure a successful recovery with the most promising settings.

    To ensure successful recovery, it is essential to follow some best practices:

    1. Regularly save model checkpoints during training to enable recovery from the last successful point.
    2. Monitor validation metrics to decide when to stop training early and prevent wasting time on non-improving models.
    3. Apply appropriate regularization techniques to prevent overfitting and enhance model stability.
    4. Utilize cross-validation to assess the model's performance and rely on completed folds if recovery is needed.
    5. Conduct hyperparameter tuning to find the best configuration and save the optimal hyperparameters for recovery purposes.

    By incorporating these tools, techniques, and best practices, you can increase the chances of successful SVM recovery.

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